From bf1ae584d8a65628cd16de30b0a1fec6f00c2eaf Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Wed, 6 Dec 2023 22:05:09 -0800
Subject: [PATCH 01/15] created simple control API and predictive control API

---
 ambersim/control/base.py               | 32 ++++++++++++
 ambersim/control/predictive_control.py | 72 ++++++++++++++++++++++++++
 2 files changed, 104 insertions(+)
 create mode 100644 ambersim/control/base.py
 create mode 100644 ambersim/control/predictive_control.py

diff --git a/ambersim/control/base.py b/ambersim/control/base.py
new file mode 100644
index 00000000..80cf973d
--- /dev/null
+++ b/ambersim/control/base.py
@@ -0,0 +1,32 @@
+import jax
+from flax import struct
+
+
+@struct.dataclass
+class ControllerParams:
+    """The parameters for generic controllers.
+
+    This is left completely empty for maximum flexibility in the API. Some examples:
+            - "Regular" inputs into feedback controllers (e.g., the state) belong here.
+            - Non-Markovian controllers can pass histories in this params object.
+            - Parameters of the controller that you may randomize/optimize go here.
+    """
+
+
+@struct.dataclass
+class Controller:
+    """The API for a generic controller.
+
+    See the notes in TrajectoryOptimizer on the generality of this class - much of the same applies.
+    """
+
+    def compute(self, ctrl_params: ControllerParams) -> jax.Array:
+        """Computes a control input.
+
+        Args:
+                ctrl_params: ControllerParams
+
+        Returns:
+                u (shape=(nu,)): The control input.
+        """
+        raise NotImplementedError
diff --git a/ambersim/control/predictive_control.py b/ambersim/control/predictive_control.py
new file mode 100644
index 00000000..33423e07
--- /dev/null
+++ b/ambersim/control/predictive_control.py
@@ -0,0 +1,72 @@
+import jax
+import jax.numpy as jnp
+from flax import struct
+from mujoco import mjx
+
+from ambersim.control.base import Controller, ControllerParams
+from ambersim.trajopt.base import TrajectoryOptimizer
+from ambersim.trajopt.shooting import VanillaPredictiveSampler, VanillaPredictiveSamplerParams
+
+# ########### #
+# GENERIC API #
+# ########### #
+
+
+@struct.dataclass
+class PredictiveControllerParams(ControllerParams):
+    """The generic API for predictive controller params."""
+
+
+@struct.dataclass
+class PredictiveController(Controller):
+    """The generic API for a predictive controller."""
+
+    trajectory_optimizer: TrajectoryOptimizer
+    model: mjx.Model
+
+    def compute(self, ctrl_params: PredictiveControllerParams) -> jax.Array:
+        """Computes a control input using forward prediction."""
+        raise NotImplementedError
+
+
+# ################### #
+# PREDICTIVE SAMPLING #
+# ################### #
+
+
+@struct.dataclass
+class PredictiveSamplingControllerParams(ControllerParams):
+    """Predictive sampling controller params."""
+
+    key: jax.Array  # random key for sampling
+    x: jax.Array  # shape=(nq + nv,) current state
+    N: int  # prediction horizon
+
+    def __post_init__(self) -> None:
+        """Post-initialization check."""
+        assert isinstance(
+            self.trajectory_optimizer, VanillaPredictiveSampler
+        ), "trajectory_optimizer must be a VanillaPredictiveSampler!"
+
+
+@struct.dataclass
+class PredictiveSamplingController(Controller):
+    """Predictive sampling controller."""
+
+    def compute(self, ctrl_params: PredictiveSamplingControllerParams) -> jax.Array:
+        """Computes a control input using forward prediction.
+
+        Args:
+                ctrl_params: Inputs into the controller.
+
+        Returns:
+                u (shape=(nu,)): The control input.
+        """
+        to_params = VanillaPredictiveSamplerParams(
+            key=ctrl_params.key,
+            x0=ctrl_params.x,
+            us_guess=jnp.zeros((ctrl_params.N, self.model.nu)),
+        )
+        _, us_star = self.trajectory_optimizer.optimize(to_params)
+        u = us_star[0, :]  # (nu,)
+        return u

From b8041ea3c317c3c04131a93608aa75a130f7f79c Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Wed, 6 Dec 2023 22:05:34 -0800
Subject: [PATCH 02/15] rename some parameter variable names for clarity

---
 ambersim/trajopt/base.py     | 38 ++++++++++++++++++------------------
 ambersim/trajopt/shooting.py | 16 +++++++--------
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/ambersim/trajopt/base.py b/ambersim/trajopt/base.py
index 2e50975e..aa5016a0 100644
--- a/ambersim/trajopt/base.py
+++ b/ambersim/trajopt/base.py
@@ -61,7 +61,7 @@ class TrajectoryOptimizer:
     raising a NotImplementedError.
     """
 
-    def optimize(self, params: TrajectoryOptimizerParams) -> Tuple[jax.Array, jax.Array, jax.Array]:
+    def optimize(self, to_params: TrajectoryOptimizerParams) -> Tuple[jax.Array, jax.Array, jax.Array]:
         """Optimizes a trajectory.
 
         The shapes of the outputs include (?) because we may choose to return non-zero-order-hold parameterizations of
@@ -69,7 +69,7 @@ def optimize(self, params: TrajectoryOptimizerParams) -> Tuple[jax.Array, jax.Ar
         control inputs over the trajectory as is done in the gradient-based methods of MJPC).
 
         Args:
-            params: The parameters of the trajectory optimizer.
+            to_params: The parameters of the trajectory optimizer.
 
         Returns:
             xs_star (shape=(N + 1, nq + nv) or (?)): The optimized trajectory.
@@ -102,22 +102,22 @@ class CostFunction:
     (4) histories of higher-order derivatives can be useful for updating their current estimates, e.g., BFGS.
     """
 
-    def cost(self, xs: jax.Array, us: jax.Array, params: CostFunctionParams) -> Tuple[jax.Array, CostFunctionParams]:
+    def cost(self, xs: jax.Array, us: jax.Array, cf_params: CostFunctionParams) -> Tuple[jax.Array, CostFunctionParams]:
         """Computes the cost of a trajectory.
 
         Args:
             xs (shape=(N + 1, nq + nv)): The state trajectory.
             us (shape=(N, nu)): The controls over the trajectory.
-            params: The parameters of the cost function.
+            cf_params: The parameters of the cost function.
 
         Returns:
             val (shape=(,)): The cost of the trajectory.
-            new_params: The updated parameters of the cost function.
+            new_cf_params: The updated parameters of the cost function.
         """
         raise NotImplementedError
 
     def grad(
-        self, xs: jax.Array, us: jax.Array, params: CostFunctionParams
+        self, xs: jax.Array, us: jax.Array, cf_params: CostFunctionParams
     ) -> Tuple[jax.Array, jax.Array, CostFunctionParams, CostFunctionParams]:
         """Computes the gradient of the cost of a trajectory.
 
@@ -127,19 +127,19 @@ def grad(
         Args:
             xs (shape=(N + 1, nq + nv)): The state trajectory.
             us (shape=(N, nu)): The controls over the trajectory.
-            params: The parameters of the cost function.
+            cf_params: The parameters of the cost function.
 
         Returns:
             gcost_xs (shape=(N + 1, nq + nv): The gradient of the cost wrt xs.
             gcost_us (shape=(N, nu)): The gradient of the cost wrt us.
-            gcost_params: The gradient of the cost wrt params.
-            new_params: The updated parameters of the cost function.
+            gcost_params: The gradient of the cost wrt cf_params.
+            new_cf_params: The updated parameters of the cost function.
         """
-        _fn = lambda xs, us, params: self.cost(xs, us, params)[0]  # only differentiate wrt the cost val
-        return grad(_fn, argnums=(0, 1, 2))(xs, us, params) + (params,)
+        _fn = lambda xs, us, cf_params: self.cost(xs, us, cf_params)[0]  # only differentiate wrt the cost val
+        return grad(_fn, argnums=(0, 1, 2))(xs, us, cf_params) + (cf_params,)
 
     def hess(
-        self, xs: jax.Array, us: jax.Array, params: CostFunctionParams
+        self, xs: jax.Array, us: jax.Array, cf_params: CostFunctionParams
     ) -> Tuple[
         jax.Array, jax.Array, CostFunctionParams, jax.Array, CostFunctionParams, CostFunctionParams, CostFunctionParams
     ]:
@@ -153,20 +153,20 @@ def hess(
         Args:
             xs (shape=(N + 1, nq + nv)): The state trajectory.
             us (shape=(N, nu)): The controls over the trajectory.
-            params: The parameters of the cost function.
+            cf_params: The parameters of the cost function.
 
         Returns:
             Hcost_xsxs (shape=(N + 1, nq + nv, N + 1, nq + nv)): The Hessian of the cost wrt xs.
             Hcost_xsus (shape=(N + 1, nq + nv, N, nu)): The Hessian of the cost wrt xs and us.
-            Hcost_xsparams: The Hessian of the cost wrt xs and params.
+            Hcost_xsparams: The Hessian of the cost wrt xs and cf_params.
             Hcost_usus (shape=(N, nu, N, nu)): The Hessian of the cost wrt us.
-            Hcost_usparams: The Hessian of the cost wrt us and params.
-            Hcost_paramsall: The Hessian of the cost wrt params and everything else.
+            Hcost_usparams: The Hessian of the cost wrt us and cf_params.
+            Hcost_paramsall: The Hessian of the cost wrt cf_params and everything else.
             new_params: The updated parameters of the cost function.
         """
-        _fn = lambda xs, us, params: self.cost(xs, us, params)[0]  # only differentiate wrt the cost val
-        hessians = hessian(_fn, argnums=(0, 1, 2))(xs, us, params)
+        _fn = lambda xs, us, cf_params: self.cost(xs, us, cf_params)[0]  # only differentiate wrt the cost val
+        hessians = hessian(_fn, argnums=(0, 1, 2))(xs, us, cf_params)
         Hcost_xsxs, Hcost_xsus, Hcost_xsparams = hessians[0]
         _, Hcost_usus, Hcost_usparams = hessians[1]
         Hcost_paramsall = hessians[2]
-        return Hcost_xsxs, Hcost_xsus, Hcost_xsparams, Hcost_usus, Hcost_usparams, Hcost_paramsall, params
+        return Hcost_xsxs, Hcost_xsus, Hcost_xsparams, Hcost_usus, Hcost_usparams, Hcost_paramsall, cf_params
diff --git a/ambersim/trajopt/shooting.py b/ambersim/trajopt/shooting.py
index 414917a1..0fd4abc4 100644
--- a/ambersim/trajopt/shooting.py
+++ b/ambersim/trajopt/shooting.py
@@ -77,11 +77,11 @@ def N(self) -> int:
 class ShootingAlgorithm(TrajectoryOptimizer):
     """A trajectory optimization algorithm based on shooting methods."""
 
-    def optimize(self, params: ShootingParams) -> Tuple[jax.Array, jax.Array]:
+    def optimize(self, to_params: ShootingParams) -> Tuple[jax.Array, jax.Array]:
         """Optimizes a trajectory using a shooting method.
 
         Args:
-            params: The parameters of the trajectory optimizer.
+            to_params: The parameters of the trajectory optimizer.
 
         Returns:
             xs_star (shape=(N + 1, nq) or (?)): The optimized trajectory.
@@ -116,11 +116,11 @@ class VanillaPredictiveSampler(ShootingAlgorithm):
     nsamples: int = struct.field(pytree_node=False)
     stdev: float = struct.field(pytree_node=False)  # noise scale, parameters theta_new ~ N(theta, (stdev ** 2) * I)
 
-    def optimize(self, params: VanillaPredictiveSamplerParams) -> Tuple[jax.Array, jax.Array]:
+    def optimize(self, to_params: VanillaPredictiveSamplerParams) -> Tuple[jax.Array, jax.Array]:
         """Optimizes a trajectory using a vanilla predictive sampler.
 
         Args:
-            params: The parameters of the trajectory optimizer.
+            to_params: The parameters of the trajectory optimizer.
 
         Returns:
             xs (shape=(N + 1, nq + nv)): The optimized trajectory.
@@ -131,10 +131,10 @@ def optimize(self, params: VanillaPredictiveSamplerParams) -> Tuple[jax.Array, j
         nsamples = self.nsamples
         stdev = self.stdev
 
-        x0 = params.x0
-        us_guess = params.us_guess
-        N = params.N
-        key = params.key
+        x0 = to_params.x0
+        us_guess = to_params.us_guess
+        N = to_params.N
+        key = to_params.key
 
         # sample over the control inputs - the first sample is the guess, since it's possible that it's the best one
         noise = jnp.concatenate(

From 958a7e79fc31ae6d93635a35ea9647e83d4e2924 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Wed, 6 Dec 2023 22:07:21 -0800
Subject: [PATCH 03/15] spacing

---
 ambersim/control/base.py               | 10 +++++-----
 ambersim/control/predictive_control.py |  4 ++--
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/ambersim/control/base.py b/ambersim/control/base.py
index 80cf973d..1a2c7a4a 100644
--- a/ambersim/control/base.py
+++ b/ambersim/control/base.py
@@ -7,9 +7,9 @@ class ControllerParams:
     """The parameters for generic controllers.
 
     This is left completely empty for maximum flexibility in the API. Some examples:
-            - "Regular" inputs into feedback controllers (e.g., the state) belong here.
-            - Non-Markovian controllers can pass histories in this params object.
-            - Parameters of the controller that you may randomize/optimize go here.
+        - "Regular" inputs into feedback controllers (e.g., the state) belong here.
+        - Non-Markovian controllers can pass histories in this params object.
+        - Parameters of the controller that you may randomize/optimize go here.
     """
 
 
@@ -24,9 +24,9 @@ def compute(self, ctrl_params: ControllerParams) -> jax.Array:
         """Computes a control input.
 
         Args:
-                ctrl_params: ControllerParams
+            ctrl_params: ControllerParams
 
         Returns:
-                u (shape=(nu,)): The control input.
+            u (shape=(nu,)): The control input.
         """
         raise NotImplementedError
diff --git a/ambersim/control/predictive_control.py b/ambersim/control/predictive_control.py
index 33423e07..d898144b 100644
--- a/ambersim/control/predictive_control.py
+++ b/ambersim/control/predictive_control.py
@@ -57,10 +57,10 @@ def compute(self, ctrl_params: PredictiveSamplingControllerParams) -> jax.Array:
         """Computes a control input using forward prediction.
 
         Args:
-                ctrl_params: Inputs into the controller.
+            ctrl_params: Inputs into the controller.
 
         Returns:
-                u (shape=(nu,)): The control input.
+            u (shape=(nu,)): The control input.
         """
         to_params = VanillaPredictiveSamplerParams(
             key=ctrl_params.key,

From d8d27fb3727fcf23d7300c7dccebc1243cd43a53 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Wed, 6 Dec 2023 22:08:55 -0800
Subject: [PATCH 04/15] minor

---
 ambersim/trajopt/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ambersim/trajopt/base.py b/ambersim/trajopt/base.py
index aa5016a0..f0d17c41 100644
--- a/ambersim/trajopt/base.py
+++ b/ambersim/trajopt/base.py
@@ -162,7 +162,7 @@ def hess(
             Hcost_usus (shape=(N, nu, N, nu)): The Hessian of the cost wrt us.
             Hcost_usparams: The Hessian of the cost wrt us and cf_params.
             Hcost_paramsall: The Hessian of the cost wrt cf_params and everything else.
-            new_params: The updated parameters of the cost function.
+            new_cf_params: The updated parameters of the cost function.
         """
         _fn = lambda xs, us, cf_params: self.cost(xs, us, cf_params)[0]  # only differentiate wrt the cost val
         hessians = hessian(_fn, argnums=(0, 1, 2))(xs, us, cf_params)

From eaaff7dd23ddbb6bfc834e085928e48832f92c00 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Fri, 8 Dec 2023 11:41:51 -0800
Subject: [PATCH 05/15] [DIRTY] commit for vince to try to repro flaky segfault

---
 ambersim/control/predictive_control.py        |  42 ++-
 .../models/allegro_hand/assets/base_link.stl  | Bin 0 -> 163884 bytes
 .../allegro_hand/assets/base_link_left.stl    | Bin 0 -> 433984 bytes
 .../models/allegro_hand/assets/link_0.0.stl   | Bin 0 -> 57584 bytes
 .../models/allegro_hand/assets/link_1.0.stl   | Bin 0 -> 70084 bytes
 .../allegro_hand/assets/link_12.0_left.stl    | Bin 0 -> 217084 bytes
 .../allegro_hand/assets/link_12.0_right.stl   | Bin 0 -> 204284 bytes
 .../models/allegro_hand/assets/link_13.0.stl  | Bin 0 -> 22484 bytes
 .../models/allegro_hand/assets/link_14.0.stl  | Bin 0 -> 46884 bytes
 .../models/allegro_hand/assets/link_15.0.stl  | Bin 0 -> 43284 bytes
 .../allegro_hand/assets/link_15.0_tip.stl     | Bin 0 -> 71984 bytes
 .../models/allegro_hand/assets/link_2.0.stl   | Bin 0 -> 52884 bytes
 .../models/allegro_hand/assets/link_3.0.stl   | Bin 0 -> 18084 bytes
 .../allegro_hand/assets/link_3.0_tip.stl      | Bin 0 -> 71984 bytes
 .../models/allegro_hand/assets/link_4.0.stl   | Bin 0 -> 22884 bytes
 ambersim/models/allegro_hand/left_hand.xml    | 259 +++++++++++++++++
 ambersim/models/allegro_hand/right_hand.xml   | 260 ++++++++++++++++++
 ambersim/models/allegro_hand/scene_left.xml   |  29 ++
 ambersim/models/allegro_hand/scene_right.xml  |  29 ++
 ambersim/sim/simulation.py                    | 162 +++++++++++
 ambersim/trajopt/cost.py                      |  18 +-
 21 files changed, 777 insertions(+), 22 deletions(-)
 create mode 100644 ambersim/models/allegro_hand/assets/base_link.stl
 create mode 100644 ambersim/models/allegro_hand/assets/base_link_left.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_0.0.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_1.0.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_12.0_left.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_12.0_right.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_13.0.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_14.0.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_15.0.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_15.0_tip.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_2.0.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_3.0.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_3.0_tip.stl
 create mode 100644 ambersim/models/allegro_hand/assets/link_4.0.stl
 create mode 100644 ambersim/models/allegro_hand/left_hand.xml
 create mode 100644 ambersim/models/allegro_hand/right_hand.xml
 create mode 100644 ambersim/models/allegro_hand/scene_left.xml
 create mode 100644 ambersim/models/allegro_hand/scene_right.xml
 create mode 100644 ambersim/sim/simulation.py

diff --git a/ambersim/control/predictive_control.py b/ambersim/control/predictive_control.py
index d898144b..c662c2c1 100644
--- a/ambersim/control/predictive_control.py
+++ b/ambersim/control/predictive_control.py
@@ -1,3 +1,5 @@
+from typing import Tuple
+
 import jax
 import jax.numpy as jnp
 from flax import struct
@@ -35,23 +37,23 @@ def compute(self, ctrl_params: PredictiveControllerParams) -> jax.Array:
 
 
 @struct.dataclass
-class PredictiveSamplingControllerParams(ControllerParams):
+class PredictiveSamplingControllerParams(PredictiveControllerParams):
     """Predictive sampling controller params."""
 
     key: jax.Array  # random key for sampling
     x: jax.Array  # shape=(nq + nv,) current state
-    N: int  # prediction horizon
+    us_guess: jax.Array  # shape=(N, nu) current guess
+
+
+@struct.dataclass
+class PredictiveSamplingController(PredictiveController):
+    """Predictive sampling controller."""
 
     def __post_init__(self) -> None:
         """Post-initialization check."""
         assert isinstance(
             self.trajectory_optimizer, VanillaPredictiveSampler
-        ), "trajectory_optimizer must be a VanillaPredictiveSampler!"
-
-
-@struct.dataclass
-class PredictiveSamplingController(Controller):
-    """Predictive sampling controller."""
+        ), "trajectory_optimizer must be a VanillaPredictiveSampler!"  # TODO(ahl): this is too restrictive
 
     def compute(self, ctrl_params: PredictiveSamplingControllerParams) -> jax.Array:
         """Computes a control input using forward prediction.
@@ -65,8 +67,30 @@ def compute(self, ctrl_params: PredictiveSamplingControllerParams) -> jax.Array:
         to_params = VanillaPredictiveSamplerParams(
             key=ctrl_params.key,
             x0=ctrl_params.x,
-            us_guess=jnp.zeros((ctrl_params.N, self.model.nu)),
+            us_guess=ctrl_params.us_guess,
         )
         _, us_star = self.trajectory_optimizer.optimize(to_params)
         u = us_star[0, :]  # (nu,)
         return u
+
+    def compute_with_us_star(self, ctrl_params: PredictiveSamplingControllerParams) -> Tuple[jax.Array, jax.Array]:
+        """Computes a control input using forward prediction + the optimal sequence of guesses.
+
+        This is needed in practice because the current optimal sequence is used to warm start the sampling distribution
+        for the next call of the controller.
+
+        Args:
+            ctrl_params: Inputs into the controller.
+
+        Returns:
+            u (shape=(nu,)): The control input.
+            us_star (shape=(N, nu)): The optimal control sequence.
+        """
+        to_params = VanillaPredictiveSamplerParams(
+            key=ctrl_params.key,
+            x0=ctrl_params.x,
+            us_guess=ctrl_params.us_guess,
+        )
+        xs_star, us_star = self.trajectory_optimizer.optimize(to_params)
+        u = us_star[0, :]
+        return u, us_star
diff --git a/ambersim/models/allegro_hand/assets/base_link.stl b/ambersim/models/allegro_hand/assets/base_link.stl
new file mode 100644
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diff --git a/ambersim/models/allegro_hand/assets/base_link_left.stl b/ambersim/models/allegro_hand/assets/base_link_left.stl
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diff --git a/ambersim/models/allegro_hand/assets/link_0.0.stl b/ambersim/models/allegro_hand/assets/link_0.0.stl
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diff --git a/ambersim/models/allegro_hand/assets/link_1.0.stl b/ambersim/models/allegro_hand/assets/link_1.0.stl
new file mode 100644
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diff --git a/ambersim/models/allegro_hand/assets/link_12.0_left.stl b/ambersim/models/allegro_hand/assets/link_12.0_left.stl
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diff --git a/ambersim/models/allegro_hand/left_hand.xml b/ambersim/models/allegro_hand/left_hand.xml
new file mode 100644
index 00000000..223d5f6b
--- /dev/null
+++ b/ambersim/models/allegro_hand/left_hand.xml
@@ -0,0 +1,259 @@
+<mujoco model="allegro_left">
+  <compiler angle="radian" meshdir="assets" autolimits="true"/>
+
+  <option cone="pyramidal" impratio="10"/>
+
+  <default>
+    <default class="allegro_left">
+      <joint axis="0 1 0" damping=".1"/>
+      <position kp="1"/>
+      <geom density="800"/>
+
+      <default class="visual">
+        <geom type="mesh" contype="0" conaffinity="0" group="2" material="black"/>
+        <default class="palm_visual">
+          <geom mesh="base_link"/>
+        </default>
+        <default class="base_visual">
+          <geom mesh="link_0.0"/>
+        </default>
+        <default class="proximal_visual">
+          <geom mesh="link_1.0"/>
+        </default>
+        <default class="medial_visual">
+          <geom mesh="link_2.0"/>
+        </default>
+        <default class="distal_visual">
+          <geom mesh="link_3.0"/>
+        </default>
+        <default class="fingertip_visual">
+          <geom pos="0 0 0.0267" material="white" mesh="link_3.0_tip"/>
+        </default>
+        <default class="thumbtip_visual">
+          <geom pos="0 0 0.0423" material="white" mesh="link_15.0_tip"/>
+        </default>
+      </default>
+
+      <default class="collision">
+        <geom group="3" type="box" mass="0"/>
+        <default class="palm_collision">
+          <geom size="0.0204 0.0565 0.0475" pos="-0.0093 0 -0.0475"/>
+        </default>
+        <default class="base_collision">
+          <geom size="0.0098 0.01375 0.0082" pos="0 0 0.0082"/>
+          <default class="thumb_base_collision">
+            <geom size="0.0179 0.017 0.02275" pos="-0.0179 0.009 0.0145"/>
+          </default>
+        </default>
+        <default class="proximal_collision">
+          <geom size="0.0098 0.01375 0.027" pos="0 0 0.027"/>
+          <default class="thumb_proximal_collision">
+            <geom size="0.0098 0.01375 0.00885" pos="0 0 0.00885"/>
+          </default>
+        </default>
+        <default class="medial_collision">
+          <geom size="0.0098 0.01375 0.0192" pos="0 0 0.0192"/>
+          <default class="thumb_medial_collision">
+            <geom size="0.0098 0.01375 0.0257" pos="0 0 0.0257"/>
+          </default>
+        </default>
+        <default class="distal_collision">
+          <geom size="0.0098 0.01375 0.008" pos="0 0 0.008"/>
+          <default class="thumb_distal_collision">
+            <geom size="0.0098 0.01375 0.0157" pos="0 0 0.0157"/>
+          </default>
+        </default>
+        <default class="fingertip_collision">
+          <geom type="capsule" size="0.012 0.01" pos="0 0 0.019"/>
+          <default class="thumbtip_collision">
+            <geom type="capsule" size="0.012 0.008" pos="0 0 0.035"/>
+          </default>
+        </default>
+      </default>
+
+      <default class="base">
+        <joint axis="0 0 1" range="-0.47 0.47"/>
+        <position ctrlrange="-0.47 0.47"/>
+      </default>
+      <default class="proximal">
+        <joint range="-0.196 1.61"/>
+        <position ctrlrange="-0.196 1.61"/>
+      </default>
+      <default class="medial">
+        <joint range="-0.174 1.709"/>
+        <position ctrlrange="-0.174 1.709"/>
+      </default>
+      <default class="distal">
+        <joint range="-0.227 1.618"/>
+        <position ctrlrange="-0.227 1.618"/>
+      </default>
+      <default class="thumb_base">
+        <joint axis="1 0 0" range="0.263 1.396"/>
+        <position ctrlrange="0.263 1.396"/>
+      </default>
+      <default class="thumb_proximal">
+        <joint axis="0 0 -1" range="-0.105 1.163"/>
+        <position ctrlrange="-0.105 1.163"/>
+      </default>
+      <default class="thumb_medial">
+        <joint range="-0.189 1.644"/>
+        <position ctrlrange="-0.189 1.644"/>
+      </default>
+      <default class="thumb_distal">
+        <joint range="-0.162 1.719"/>
+        <position ctrlrange="-0.162 1.719"/>
+      </default>
+    </default>
+  </default>
+
+  <asset>
+    <material name="black" rgba="0.2 0.2 0.2 1"/>
+    <material name="white" rgba="0.9 0.9 0.9 1"/>
+
+    <mesh file="base_link_left.stl"/>
+    <mesh file="link_0.0.stl"/>
+    <mesh file="link_1.0.stl"/>
+    <mesh file="link_2.0.stl"/>
+    <mesh file="link_3.0.stl"/>
+    <mesh file="link_3.0_tip.stl"/>
+    <mesh file="link_12.0_left.stl"/>
+    <mesh file="link_13.0.stl"/>
+    <mesh file="link_14.0.stl"/>
+    <mesh file="link_15.0.stl"/>
+    <mesh file="link_15.0_tip.stl"/>
+  </asset>
+
+  <worldbody>
+    <body name="palm" quat="0 1 0 1" childclass="allegro_left">
+      <geom quat="1 -1 0 0" class="palm_visual" mesh="base_link_left"/>
+      <geom class="palm_collision"/>
+      <!-- Ring finger -->
+      <body name="rf_base" pos="0 0.0435 -0.001542" quat="0.999048 -0.0436194 0 0">
+        <joint name="rfj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="rf_proximal" pos="0 0 0.0164">
+          <joint name="rfj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="rf_medial" pos="0 0 0.054">
+            <joint name="rfj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="rf_distal" pos="0 0 0.0384">
+              <joint name="rfj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="rf_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Middle finger -->
+      <body name="mf_base" pos="0 0 0.0007">
+        <joint name="mfj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="mf_proximal" pos="0 0 0.0164">
+          <joint name="mfj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="mf_medial" pos="0 0 0.054">
+            <joint name="mfj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="mf_distal" pos="0 0 0.0384">
+              <joint name="mfj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="mf_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- First finger -->
+      <body name="ff_base" pos="0 -0.0435 -0.001542" quat="0.999048 0.0436194 0 0">
+        <joint name="ffj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="ff_proximal" pos="0 0 0.0164">
+          <joint name="ffj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="ff_medial" pos="0 0 0.054">
+            <joint name="ffj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="ff_distal" pos="0 0 0.0384">
+              <joint name="ffj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="ff_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Thumb -->
+      <body name="th_base" pos="-0.0182 -0.019333 -0.045987" quat="0.477714 0.521334 -0.521334 0.477714">
+        <joint name="thj0" class="thumb_base"/>
+        <geom quat="0 1 0 0" class="visual" mesh="link_12.0_left"/>
+        <geom class="thumb_base_collision"/>
+        <body name="th_proximal" pos="-0.027 -0.005 0.0399">
+          <joint name="thj1" class="thumb_proximal"/>
+          <geom class="visual" mesh="link_13.0"/>
+          <geom class="thumb_proximal_collision"/>
+          <body name="th_medial" pos="0 0 0.0177">
+            <joint name="thj2" class="thumb_medial"/>
+            <geom class="visual" mesh="link_14.0"/>
+            <geom class="thumb_medial_collision"/>
+            <body name="th_distal" pos="0 0 0.0514">
+              <joint name="thj3" class="thumb_distal"/>
+              <geom class="visual" mesh="link_15.0"/>
+              <geom class="thumb_distal_collision"/>
+              <body name="th_tip">
+                <geom class="thumbtip_visual"/>
+                <geom class="thumbtip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+    </body>
+  </worldbody>
+
+  <contact>
+    <exclude body1="palm" body2="ff_base"/>
+    <exclude body1="palm" body2="mf_base"/>
+    <exclude body1="palm" body2="rf_base"/>
+    <exclude body1="palm" body2="th_base"/>
+    <exclude body1="palm" body2="th_proximal"/>
+  </contact>
+
+  <actuator>
+    <motor name="ffa0" ctrllimited="true" ctrlrange="-0.7 0.7" joint="ffj0" class="base"/>
+    <motor name="ffa1" ctrllimited="true" ctrlrange="-0.7 0.7" joint="ffj1" class="proximal"/>
+    <motor name="ffa2" ctrllimited="true" ctrlrange="-0.7 0.7" joint="ffj2" class="medial"/>
+    <motor name="ffa3" ctrllimited="true" ctrlrange="-0.7 0.7" joint="ffj3" class="distal"/>
+    <motor name="mfa0" ctrllimited="true" ctrlrange="-0.7 0.7" joint="mfj0" class="base"/>
+    <motor name="mfa1" ctrllimited="true" ctrlrange="-0.7 0.7" joint="mfj1" class="proximal"/>
+    <motor name="mfa2" ctrllimited="true" ctrlrange="-0.7 0.7" joint="mfj2" class="medial"/>
+    <motor name="mfa3" ctrllimited="true" ctrlrange="-0.7 0.7" joint="mfj3" class="distal"/>
+    <motor name="rfa0" ctrllimited="true" ctrlrange="-0.7 0.7" joint="rfj0" class="base"/>
+    <motor name="rfa1" ctrllimited="true" ctrlrange="-0.7 0.7" joint="rfj1" class="proximal"/>
+    <motor name="rfa2" ctrllimited="true" ctrlrange="-0.7 0.7" joint="rfj2" class="medial"/>
+    <motor name="rfa3" ctrllimited="true" ctrlrange="-0.7 0.7" joint="rfj3" class="distal"/>
+    <motor name="tha0" ctrllimited="true" ctrlrange="-0.7 0.7" joint="thj0" class="thumb_base"/>
+    <motor name="tha1" ctrllimited="true" ctrlrange="-0.7 0.7" joint="thj1" class="thumb_proximal"/>
+    <motor name="tha2" ctrllimited="true" ctrlrange="-0.7 0.7" joint="thj2" class="thumb_medial"/>
+    <motor name="tha3" ctrllimited="true" ctrlrange="-0.7 0.7" joint="thj3" class="thumb_distal"/>
+  </actuator>
+</mujoco>
diff --git a/ambersim/models/allegro_hand/right_hand.xml b/ambersim/models/allegro_hand/right_hand.xml
new file mode 100644
index 00000000..cc6f4dd0
--- /dev/null
+++ b/ambersim/models/allegro_hand/right_hand.xml
@@ -0,0 +1,260 @@
+<mujoco model="allegro_right">
+  <compiler angle="radian" meshdir="assets" autolimits="true"/>
+
+  <option cone="pyramidal" impratio="10"/>
+
+  <default>
+    <default class="allegro_right">
+      <joint axis="0 1 0" damping=".1"/>
+      <position kp="1"/>
+      <geom density="800"/>
+
+      <default class="visual">
+        <geom type="mesh" contype="0" conaffinity="0" group="2" material="black"/>
+        <default class="palm_visual">
+          <geom mesh="base_link"/>
+        </default>
+        <default class="base_visual">
+          <geom mesh="link_0.0"/>
+        </default>
+        <default class="proximal_visual">
+          <geom mesh="link_1.0"/>
+        </default>
+        <default class="medial_visual">
+          <geom mesh="link_2.0"/>
+        </default>
+        <default class="distal_visual">
+          <geom mesh="link_3.0"/>
+        </default>
+        <default class="fingertip_visual">
+          <geom pos="0 0 0.0267" material="white" mesh="link_3.0_tip"/>
+        </default>
+        <default class="thumbtip_visual">
+          <geom pos="0 0 0.0423" material="white" mesh="link_15.0_tip"/>
+        </default>
+      </default>
+
+      <default class="collision">
+        <geom group="3" type="box" mass="0"/>
+        <default class="palm_collision">
+          <geom size="0.0204 0.0565 0.0475" pos="-0.0093 0 -0.0475"/>
+        </default>
+        <default class="base_collision">
+          <geom size="0.0098 0.01375 0.0082" pos="0 0 0.0082"/>
+          <default class="thumb_base_collision">
+            <geom size="0.0179 0.017 0.02275" pos="-0.0179 0.009 0.0145"/>
+          </default>
+        </default>
+        <default class="proximal_collision">
+          <geom size="0.0098 0.01375 0.027" pos="0 0 0.027"/>
+          <default class="thumb_proximal_collision">
+            <geom size="0.0098 0.01375 0.00885" pos="0 0 0.00885"/>
+          </default>
+        </default>
+        <default class="medial_collision">
+          <geom size="0.0098 0.01375 0.0192" pos="0 0 0.0192"/>
+          <default class="thumb_medial_collision">
+            <geom size="0.0098 0.01375 0.0257" pos="0 0 0.0257"/>
+          </default>
+        </default>
+        <default class="distal_collision">
+          <geom size="0.0098 0.01375 0.008" pos="0 0 0.008"/>
+          <default class="thumb_distal_collision">
+            <geom size="0.0098 0.01375 0.0157" pos="0 0 0.0157"/>
+          </default>
+        </default>
+        <default class="fingertip_collision">
+          <geom type="capsule" size="0.012 0.01" pos="0 0 0.019"/>
+          <default class="thumbtip_collision">
+            <geom type="capsule" size="0.012 0.008" pos="0 0 0.035"/>
+          </default>
+        </default>
+      </default>
+
+      <default class="base">
+        <joint axis="0 0 1" range="-0.47 0.47"/>
+        <position ctrlrange="-0.47 0.47"/>
+      </default>
+      <default class="proximal">
+        <joint range="-0.196 1.61"/>
+        <position ctrlrange="-0.196 1.61"/>
+      </default>
+      <default class="medial">
+        <joint range="-0.174 1.709"/>
+        <position ctrlrange="-0.174 1.709"/>
+      </default>
+      <default class="distal">
+        <joint range="-0.227 1.618"/>
+        <position ctrlrange="-0.227 1.618"/>
+      </default>
+      <default class="thumb_base">
+        <joint axis="-1 0 0" range="0.263 1.396"/>
+        <position ctrlrange="0.263 1.396"/>
+      </default>
+      <default class="thumb_proximal">
+        <joint axis="0 0 1" range="-0.105 1.163"/>
+        <position ctrlrange="-0.105 1.163"/>
+      </default>
+      <default class="thumb_medial">
+        <joint range="-0.189 1.644"/>
+        <position ctrlrange="-0.189 1.644"/>
+      </default>
+      <default class="thumb_distal">
+        <joint range="-0.162 1.719"/>
+        <position ctrlrange="-0.162 1.719"/>
+      </default>
+    </default>
+  </default>
+
+  <asset>
+    <material name="black" rgba="0.2 0.2 0.2 1"/>
+    <material name="white" rgba="0.9 0.9 0.9 1"/>
+
+    <mesh file="base_link.stl"/>
+    <mesh file="link_0.0.stl"/>
+    <mesh file="link_1.0.stl"/>
+    <mesh file="link_2.0.stl"/>
+    <mesh file="link_3.0.stl"/>
+    <mesh file="link_3.0_tip.stl"/>
+    <mesh file="link_12.0_right.stl"/>
+    <mesh file="link_13.0.stl"/>
+    <mesh file="link_14.0.stl"/>
+    <mesh file="link_15.0.stl"/>
+    <mesh file="link_15.0_tip.stl"/>
+  </asset>
+
+  <worldbody>
+    <body name="palm" quat="0 1 0 1" childclass="allegro_right">
+      <!-- <inertial mass="0.4154" pos="0 0 0.0475" diaginertia="1e-4 1e-4 1e-4"/> -->
+      <geom class="palm_visual" mesh="base_link"/>
+      <geom class="palm_collision"/>
+      <!-- First finger -->
+      <body name="ff_base" pos="0 0.0435 -0.001542" quat="0.999048 -0.0436194 0 0">
+        <joint name="ffj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="ff_proximal" pos="0 0 0.0164">
+          <joint name="ffj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="ff_medial" pos="0 0 0.054">
+            <joint name="ffj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="ff_distal" pos="0 0 0.0384">
+              <joint name="ffj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="ff_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Middle finger -->
+      <body name="mf_base" pos="0 0 0.0007">
+        <joint name="mfj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="mf_proximal" pos="0 0 0.0164">
+          <joint name="mfj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="mf_medial" pos="0 0 0.054">
+            <joint name="mfj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="mf_distal" pos="0 0 0.0384">
+              <joint name="mfj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="mf_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Ring finger -->
+      <body name="rf_base" pos="0 -0.0435 -0.001542" quat="0.999048 0.0436194 0 0">
+        <joint name="rfj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="rf_proximal" pos="0 0 0.0164">
+          <joint name="rfj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="rf_medial" pos="0 0 0.054">
+            <joint name="rfj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="rf_distal" pos="0 0 0.0384">
+              <joint name="rfj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="rf_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Thumb -->
+      <body name="th_base" pos="-0.0182 0.019333 -0.045987" quat="0.477714 -0.521334 -0.521334 -0.477714">
+        <joint name="thj0" class="thumb_base"/>
+        <geom class="visual" mesh="link_12.0_right"/>
+        <geom class="thumb_base_collision"/>
+        <body name="th_proximal" pos="-0.027 0.005 0.0399">
+          <joint name="thj1" class="thumb_proximal"/>
+          <geom class="visual" mesh="link_13.0"/>
+          <geom class="thumb_proximal_collision"/>
+          <body name="th_medial" pos="0 0 0.0177">
+            <joint name="thj2" class="thumb_medial"/>
+            <geom class="visual" mesh="link_14.0"/>
+            <geom class="thumb_medial_collision"/>
+            <body name="th_distal" pos="0 0 0.0514">
+              <joint name="thj3" class="thumb_distal"/>
+              <geom class="visual" mesh="link_15.0"/>
+              <geom class="thumb_distal_collision"/>
+              <body name="th_tip">
+                <geom class="thumbtip_visual"/>
+                <geom class="thumbtip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+    </body>
+  </worldbody>
+
+  <contact>
+    <exclude body1="palm" body2="ff_base"/>
+    <exclude body1="palm" body2="mf_base"/>
+    <exclude body1="palm" body2="rf_base"/>
+    <exclude body1="palm" body2="th_base"/>
+    <exclude body1="palm" body2="th_proximal"/>
+  </contact>
+
+  <actuator>
+    <motor name="ffa0" ctrllimited="true" ctrlrange="-0.7 0.7" joint="ffj0" class="base"/>
+    <motor name="ffa1" ctrllimited="true" ctrlrange="-0.7 0.7" joint="ffj1" class="proximal"/>
+    <motor name="ffa2" ctrllimited="true" ctrlrange="-0.7 0.7" joint="ffj2" class="medial"/>
+    <motor name="ffa3" ctrllimited="true" ctrlrange="-0.7 0.7" joint="ffj3" class="distal"/>
+    <motor name="mfa0" ctrllimited="true" ctrlrange="-0.7 0.7" joint="mfj0" class="base"/>
+    <motor name="mfa1" ctrllimited="true" ctrlrange="-0.7 0.7" joint="mfj1" class="proximal"/>
+    <motor name="mfa2" ctrllimited="true" ctrlrange="-0.7 0.7" joint="mfj2" class="medial"/>
+    <motor name="mfa3" ctrllimited="true" ctrlrange="-0.7 0.7" joint="mfj3" class="distal"/>
+    <motor name="rfa0" ctrllimited="true" ctrlrange="-0.7 0.7" joint="rfj0" class="base"/>
+    <motor name="rfa1" ctrllimited="true" ctrlrange="-0.7 0.7" joint="rfj1" class="proximal"/>
+    <motor name="rfa2" ctrllimited="true" ctrlrange="-0.7 0.7" joint="rfj2" class="medial"/>
+    <motor name="rfa3" ctrllimited="true" ctrlrange="-0.7 0.7" joint="rfj3" class="distal"/>
+    <motor name="tha0" ctrllimited="true" ctrlrange="-0.7 0.7" joint="thj0" class="thumb_base"/>
+    <motor name="tha1" ctrllimited="true" ctrlrange="-0.7 0.7" joint="thj1" class="thumb_proximal"/>
+    <motor name="tha2" ctrllimited="true" ctrlrange="-0.7 0.7" joint="thj2" class="thumb_medial"/>
+    <motor name="tha3" ctrllimited="true" ctrlrange="-0.7 0.7" joint="thj3" class="thumb_distal"/>
+  </actuator>
+</mujoco>
diff --git a/ambersim/models/allegro_hand/scene_left.xml b/ambersim/models/allegro_hand/scene_left.xml
new file mode 100644
index 00000000..bce144ed
--- /dev/null
+++ b/ambersim/models/allegro_hand/scene_left.xml
@@ -0,0 +1,29 @@
+<mujoco model="left_allegro_hand scene">
+  <include file="left_hand.xml"/>
+
+  <statistic center="0 0 0" extent="0.3"/>
+
+  <visual>
+    <rgba haze="0.15 0.25 0.35 1"/>
+    <quality shadowsize="8192"/>
+    <global azimuth="220" elevation="-40"/>
+  </visual>
+
+  <asset>
+    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
+      markrgb="0.8 0.8 0.8" width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+  </asset>
+
+  <worldbody>
+    <light pos="0 0 1"/>
+    <light pos="0.3 0 1.5" dir="0 0 -1" directional="true"/>
+    <geom name="floor" pos="0 0 -0.1" size="0 0 0.05" type="plane" material="groundplane"/>
+    <body name="object" pos="0 0 0.1">
+      <freejoint/>
+      <geom type="ellipsoid" size="0.03 0.04 0.02" rgba="0.5 0.7 0.5 1" condim="6" priority="1"
+        friction="0.7 0.002 0.002"/>
+    </body>
+  </worldbody>
+</mujoco>
diff --git a/ambersim/models/allegro_hand/scene_right.xml b/ambersim/models/allegro_hand/scene_right.xml
new file mode 100644
index 00000000..36380ece
--- /dev/null
+++ b/ambersim/models/allegro_hand/scene_right.xml
@@ -0,0 +1,29 @@
+<mujoco model="right_allegro_hand scene">
+  <include file="right_hand.xml"/>
+
+  <statistic center="0 0 0" extent="0.3"/>
+
+  <visual>
+    <rgba haze="0.15 0.25 0.35 1"/>
+    <quality shadowsize="8192"/>
+    <global azimuth="130" elevation="-40"/>
+  </visual>
+
+  <asset>
+    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
+      markrgb="0.8 0.8 0.8" width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+  </asset>
+
+  <worldbody>
+    <light pos="0 0 1"/>
+    <light pos="0.3 0 1.5" dir="0 0 -1" directional="true"/>
+    <geom name="floor" pos="0 0 -0.1" size="0 0 0.05" type="plane" material="groundplane"/>
+    <body name="object" pos="0 0 0.1">
+      <freejoint/>
+      <geom type="ellipsoid" size="0.03 0.04 0.02" rgba="0.5 0.7 0.5 1" condim="6" priority="1"
+        friction="0.7 0.002 0.002"/>
+    </body>
+  </worldbody>
+</mujoco>
diff --git a/ambersim/sim/simulation.py b/ambersim/sim/simulation.py
new file mode 100644
index 00000000..fd4f7c91
--- /dev/null
+++ b/ambersim/sim/simulation.py
@@ -0,0 +1,162 @@
+import jax
+import jax.numpy as jnp
+from jax import jit, lax
+from mujoco import mjx
+
+from ambersim.control.predictive_control import PredictiveSamplingController, PredictiveSamplingControllerParams
+
+"""This file contains simulation utils for various controllers."""
+
+
+def simulate_predictive_sampling_controller(
+    model: mjx.Model,
+    controller: PredictiveSamplingController,
+    x0: jax.Array,
+    num_steps: int,
+    N: int,
+    seed: int = 0,
+    physics_steps_per_control_step: int = 1,
+) -> None:
+    """Simulates a closed-loop system.
+
+    Args:
+        model: The model. Properties like the timestep are set in here.
+        controller: The controller.
+        x0: The initial state.
+        num_steps: The number of steps to simulate for.
+        seed: The random seed.
+        physics_steps_per_control_step: The number of physics steps to take per control step.
+
+    Returns:
+        data: The internal data of the model after the simulation.
+        xs: The state trajectory.
+    """
+    # initial setup
+    print("Initial setup...")
+    dt = model.opt.timestep
+
+    data = mjx.make_data(model)
+    data = data.replace(qpos=x0[: model.nq], qvel=x0[model.nq :])  # setting the initial state.
+    data = mjx.forward(model, data)  # setting other internal states like acceleration without integrating
+
+    xs_hist = jnp.zeros((num_steps + 1, model.nq + model.nv))
+    xs_hist = xs_hist.at[0, :].set(x0)
+
+    us_hist = jnp.zeros((num_steps, model.nu))
+    key = jax.random.PRNGKey(seed)
+
+    jit_compute = jit(
+        lambda key, x_meas, us_guess: controller.compute_with_us_star(
+            PredictiveSamplingControllerParams(key=key, x=x_meas, us_guess=us_guess)
+        )
+    )
+    jit_step = jit(lambda data, u: mjx.step(model, data.replace(ctrl=u)))
+
+    us_guess = jnp.zeros((N, model.nu))
+    import time
+
+    times = []
+    for i in range(num_steps // physics_steps_per_control_step):
+        start = time.time()
+        # computing the control input
+        x_meas = jnp.concatenate((data.qpos, data.qvel))
+        if i == 0:
+            print("Compiling the compute function...")
+        u, us_guess = jit_compute(key, x_meas, us_guess)
+        if i == 0:
+            print("Compiled the compute function!")
+
+        # simulating the system forward
+        for j in range(physics_steps_per_control_step):
+            print(f"t: {(i * physics_steps_per_control_step + j) * dt:.2f} | u: {u}")
+            if i == 0 and j == 0:
+                print("Compiling the step function...")
+            breakpoint()
+            data = jit_step(data, u)  # <-- segfaults here! non-jitted version doesn't segfault
+            if i == 0 and j == 0:
+                print("Compiled the step function!")
+            us_hist = us_hist.at[i, :].set(u)
+            xs_hist = xs_hist.at[i + 1, :].set(jnp.concatenate((data.qpos, data.qvel)))
+
+        key = jax.random.split(key)[0]
+        end = time.time()
+        times.append(end - start)
+    return data, xs_hist, us_hist, times  # [DEBUG] return iteration times
+
+
+if __name__ == "__main__":
+    # TODO(ahl): move this into a proper script
+    import time
+
+    import mujoco
+    import mujoco.viewer
+    import numpy as np
+    from mujoco.mjx._src.types import DisableBit
+
+    from ambersim.trajopt.cost import StaticGoalQuadraticCost
+    from ambersim.trajopt.shooting import VanillaPredictiveSampler
+    from ambersim.utils.io_utils import load_mj_model_from_file, mj_to_mjx_model_and_data
+
+    # loading model + defining the predictive controller
+    # mj_model = load_mj_model_from_file("models/barrett_hand/bh280.xml")
+    mj_model = load_mj_model_from_file("models/allegro_hand/right_hand.xml")
+    mj_model.opt.timestep = 0.003
+    model, _ = mj_to_mjx_model_and_data(mj_model)
+    model = model.replace(
+        opt=model.opt.replace(
+            timestep=0.003,  # dt
+            iterations=1,  # number of Newton steps to take during solve
+            ls_iterations=4,  # number of line search iterations along step direction
+            integrator=0,  # Euler semi-implicit integration
+            solver=2,  # Newton solver
+            disableflags=DisableBit.CONTACT,  # disable contact for this test
+        )
+    )
+    cost_function = StaticGoalQuadraticCost(
+        Q=jnp.diag(jnp.array([10.0] * model.nq + [1.0] * model.nv)),
+        Qf=jnp.diag(jnp.array([10.0] * model.nq + [1.0] * model.nv)),
+        R=jnp.eye(model.nu),
+        xg=jnp.zeros(model.nq + model.nv),
+    )
+    nsamples = 1  # N=10, dt=0.003, nsamples=1000 just barely hits realtime rates
+    stdev = 0.25
+    ps = VanillaPredictiveSampler(model=model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
+
+    N = 10
+    controller = PredictiveSamplingController(trajectory_optimizer=ps, model=model)
+
+    print("Controller created! Simulating...")
+
+    # simulating forward
+    key = jax.random.PRNGKey(1234)
+    # x0 = jnp.array([0.14021026, 0.04142465, 0.99314054, -0.00491294, -0.00487147,  0.81353587, -0.00491293, -0.00487143, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
+    x0 = 2.0 * (jax.random.uniform(key, shape=(model.nq + model.nv,)) - 0.5)
+    T = 3.0
+    num_steps = int(T / model.opt.timestep)
+    data, xs, us, times = simulate_predictive_sampling_controller(
+        model, controller, x0, num_steps, N, seed=1337, physics_steps_per_control_step=N
+    )  # just testing warm starting the sim
+    print(np.mean(times[1:]))
+
+    # post-hoc visualization
+    i = 0
+    mj_data = mujoco.MjData(mj_model)
+    mj_data.qpos[:] = x0[: model.nq]
+    mj_data.qvel[:] = x0[model.nq :]
+    dt = model.opt.timestep
+    with mujoco.viewer.launch_passive(mj_model, mj_data) as viewer:
+        while viewer.is_running():
+            start = time.time()
+            ui = us[i, :]
+            mj_data.ctrl[:] = ui
+            if i <= int(T / dt):
+                mujoco.mj_step(mj_model, mj_data)
+                print(f"t: {i * dt:.2f} | qpos: {mj_data.qpos}")
+                print(f"t: {i * dt:.2f} | cost: {mj_data.qpos @ mj_data.qpos + mj_data.qvel @ mj_data.qvel:.2f}")
+                viewer.sync()
+                i += 1
+                elapsed = time.time() - start
+                if elapsed < mj_model.opt.timestep:
+                    time.sleep(mj_model.opt.timestep)
+
+    breakpoint()
diff --git a/ambersim/trajopt/cost.py b/ambersim/trajopt/cost.py
index fa560128..c50fb842 100644
--- a/ambersim/trajopt/cost.py
+++ b/ambersim/trajopt/cost.py
@@ -10,6 +10,7 @@
 """A collection of common cost functions."""
 
 
+@struct.dataclass
 class StaticGoalQuadraticCost(CostFunction):
     """A quadratic cost function that penalizes the distance to a static goal.
 
@@ -19,19 +20,10 @@ class StaticGoalQuadraticCost(CostFunction):
     dense.
     """
 
-    def __init__(self, Q: jax.Array, Qf: jax.Array, R: jax.Array, xg: jax.Array) -> None:
-        """Initializes a quadratic cost function.
-
-        Args:
-            Q (shape=(nx, nx)): The state cost matrix.
-            Qf (shape=(nx, nx)): The final state cost matrix.
-            R (shape=(nu, nu)): The control cost matrix.
-            xg (shape=(nq,)): The goal state.
-        """
-        self.Q = Q
-        self.Qf = Qf
-        self.R = R
-        self.xg = xg
+    Q: jax.Array  # shape=(nx, nx) state cost matrix
+    Qf: jax.Array  # shape=(nx, nx) final state cost matrix
+    R: jax.Array  # shape=(nu, nu) control cost matrix
+    xg: jax.Array  # shape=(nq + nv,) goal state
 
     @staticmethod
     def batch_quadform(bs: jax.Array, A: jax.Array) -> jax.Array:

From 26aae8af7ca6298e6ad7e5604ecb4c897b7d8745 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Fri, 8 Dec 2023 18:22:58 -0800
Subject: [PATCH 06/15] [DIRTY] extremely dirty commit with us messing around
 for vince to replicate c++ version

---
 ambersim/sim/simulation.py | 44 ++++++++++++++++++++++----------------
 1 file changed, 26 insertions(+), 18 deletions(-)

diff --git a/ambersim/sim/simulation.py b/ambersim/sim/simulation.py
index fd4f7c91..ea6b7a73 100644
--- a/ambersim/sim/simulation.py
+++ b/ambersim/sim/simulation.py
@@ -20,7 +20,7 @@ def simulate_predictive_sampling_controller(
     """Simulates a closed-loop system.
 
     Args:
-        model: The model. Properties like the timestep are set in here.
+        model: The "real" model. Properties like the timestep are set in here.
         controller: The controller.
         x0: The initial state.
         num_steps: The number of steps to simulate for.
@@ -52,7 +52,8 @@ def simulate_predictive_sampling_controller(
     )
     jit_step = jit(lambda data, u: mjx.step(model, data.replace(ctrl=u)))
 
-    us_guess = jnp.zeros((N, model.nu))
+    # us_guess = jnp.zeros((N, model.nu))
+    us_guess = jnp.array([0.0, 0.2, 0.2, 0.2, 0.0, 0.2, 0.2, 0.2, 0.0, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1, 0.1])
     import time
 
     times = []
@@ -65,13 +66,14 @@ def simulate_predictive_sampling_controller(
         u, us_guess = jit_compute(key, x_meas, us_guess)
         if i == 0:
             print("Compiled the compute function!")
+        end = time.time()
+        times.append(end - start)
 
         # simulating the system forward
         for j in range(physics_steps_per_control_step):
             print(f"t: {(i * physics_steps_per_control_step + j) * dt:.2f} | u: {u}")
             if i == 0 and j == 0:
                 print("Compiling the step function...")
-            breakpoint()
             data = jit_step(data, u)  # <-- segfaults here! non-jitted version doesn't segfault
             if i == 0 and j == 0:
                 print("Compiled the step function!")
@@ -79,8 +81,7 @@ def simulate_predictive_sampling_controller(
             xs_hist = xs_hist.at[i + 1, :].set(jnp.concatenate((data.qpos, data.qvel)))
 
         key = jax.random.split(key)[0]
-        end = time.time()
-        times.append(end - start)
+
     return data, xs_hist, us_hist, times  # [DEBUG] return iteration times
 
 
@@ -104,26 +105,33 @@ def simulate_predictive_sampling_controller(
     model, _ = mj_to_mjx_model_and_data(mj_model)
     model = model.replace(
         opt=model.opt.replace(
-            timestep=0.003,  # dt
+            timestep=0.003,  # dt, "framerate of reality"
             iterations=1,  # number of Newton steps to take during solve
             ls_iterations=4,  # number of line search iterations along step direction
             integrator=0,  # Euler semi-implicit integration
             solver=2,  # Newton solver
-            disableflags=DisableBit.CONTACT,  # disable contact for this test
+            # disableflags=DisableBit.CONTACT,  # disable contact for this test
         )
     )
     cost_function = StaticGoalQuadraticCost(
-        Q=jnp.diag(jnp.array([10.0] * model.nq + [1.0] * model.nv)),
-        Qf=jnp.diag(jnp.array([10.0] * model.nq + [1.0] * model.nv)),
-        R=jnp.eye(model.nu),
-        xg=jnp.zeros(model.nq + model.nv),
+        Q=jnp.diag(jnp.array([10.0] * model.nq + [0.1] * model.nv)),
+        Qf=jnp.diag(jnp.array([10.0] * model.nq + [0.1] * model.nv)),
+        R=0.0 * jnp.eye(model.nu),
+        xg=jnp.concatenate(
+            (jnp.array([0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 1.0, 0.8, 0.5, 0.5]), jnp.zeros(16))
+        ),
     )
-    nsamples = 1  # N=10, dt=0.003, nsamples=1000 just barely hits realtime rates
-    stdev = 0.25
+    nsamples = 1000  # N=10, dt=0.003, nsamples=1000 just barely hits realtime rates
+    stdev = 0.2
     ps = VanillaPredictiveSampler(model=model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
 
     N = 10
-    controller = PredictiveSamplingController(trajectory_optimizer=ps, model=model)
+    ctrl_model = model.replace(
+        opt=model.opt.replace(
+            timestep=0.03,
+        )
+    )
+    controller = PredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
 
     print("Controller created! Simulating...")
 
@@ -134,7 +142,7 @@ def simulate_predictive_sampling_controller(
     T = 3.0
     num_steps = int(T / model.opt.timestep)
     data, xs, us, times = simulate_predictive_sampling_controller(
-        model, controller, x0, num_steps, N, seed=1337, physics_steps_per_control_step=N
+        model, controller, x0, num_steps, N, seed=1337, physics_steps_per_control_step=2
     )  # just testing warm starting the sim
     print(np.mean(times[1:]))
 
@@ -151,12 +159,12 @@ def simulate_predictive_sampling_controller(
             mj_data.ctrl[:] = ui
             if i <= int(T / dt):
                 mujoco.mj_step(mj_model, mj_data)
-                print(f"t: {i * dt:.2f} | qpos: {mj_data.qpos}")
-                print(f"t: {i * dt:.2f} | cost: {mj_data.qpos @ mj_data.qpos + mj_data.qvel @ mj_data.qvel:.2f}")
+                # print(f"t: {i * dt:.2f} | qpos: {mj_data.qpos}")
+                # print(f"t: {i * dt:.2f} | cost: {mj_data.qpos @ mj_data.qpos + mj_data.qvel @ mj_data.qvel:.2f}")
                 viewer.sync()
                 i += 1
                 elapsed = time.time() - start
                 if elapsed < mj_model.opt.timestep:
-                    time.sleep(mj_model.opt.timestep)
+                    time.sleep(5 * mj_model.opt.timestep)
 
     breakpoint()

From e382bb6d400ad11602caf688f325b549ddfbdf3c Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sat, 9 Dec 2023 12:39:16 -0800
Subject: [PATCH 07/15] [DIRTY] stable predictive sampling for regulating to
 q_goal

---
 ambersim/sim/simulation.py | 121 +++++++++++++++++++++++++------------
 1 file changed, 81 insertions(+), 40 deletions(-)

diff --git a/ambersim/sim/simulation.py b/ambersim/sim/simulation.py
index ea6b7a73..de356437 100644
--- a/ambersim/sim/simulation.py
+++ b/ambersim/sim/simulation.py
@@ -92,79 +92,120 @@ def simulate_predictive_sampling_controller(
     import mujoco
     import mujoco.viewer
     import numpy as np
+    from jax.experimental import mesh_utils
+    from jax.experimental.shard_map import shard_map
+    from jax.sharding import Mesh, NamedSharding
+    from jax.sharding import PartitionSpec as P
     from mujoco.mjx._src.types import DisableBit
 
     from ambersim.trajopt.cost import StaticGoalQuadraticCost
     from ambersim.trajopt.shooting import VanillaPredictiveSampler
     from ambersim.utils.io_utils import load_mj_model_from_file, mj_to_mjx_model_and_data
 
+    # sharding logic
+    devices = np.array(jax.devices())
+    mesh = Mesh(devices, ("i",))
+
     # loading model + defining the predictive controller
     # mj_model = load_mj_model_from_file("models/barrett_hand/bh280.xml")
     mj_model = load_mj_model_from_file("models/allegro_hand/right_hand.xml")
-    mj_model.opt.timestep = 0.003
-    model, _ = mj_to_mjx_model_and_data(mj_model)
-    model = model.replace(
-        opt=model.opt.replace(
-            timestep=0.003,  # dt, "framerate of reality"
+    mj_model.opt.timestep = 0.001  # dt, "framerate of reality"
+
+    ctrl_model, _ = mj_to_mjx_model_and_data(mj_model)
+    ctrl_model = ctrl_model.replace(
+        opt=ctrl_model.opt.replace(
+            timestep=0.015,  # dt for each step in the controller's internal model
             iterations=1,  # number of Newton steps to take during solve
-            ls_iterations=4,  # number of line search iterations along step direction
+            ls_iterations=1,  # number of line search iterations along step direction
             integrator=0,  # Euler semi-implicit integration
             solver=2,  # Newton solver
             # disableflags=DisableBit.CONTACT,  # disable contact for this test
         )
     )
     cost_function = StaticGoalQuadraticCost(
-        Q=jnp.diag(jnp.array([10.0] * model.nq + [0.1] * model.nv)),
-        Qf=jnp.diag(jnp.array([10.0] * model.nq + [0.1] * model.nv)),
-        R=0.0 * jnp.eye(model.nu),
+        Q=jnp.diag(jnp.array([10.0] * ctrl_model.nq + [0.01] * ctrl_model.nv)),
+        Qf=jnp.diag(jnp.array([10.0] * ctrl_model.nq + [0.01] * ctrl_model.nv)),
+        R=0.001 * jnp.eye(ctrl_model.nu),
         xg=jnp.concatenate(
             (jnp.array([0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 1.0, 0.8, 0.5, 0.5]), jnp.zeros(16))
         ),
     )
-    nsamples = 1000  # N=10, dt=0.003, nsamples=1000 just barely hits realtime rates
-    stdev = 0.2
-    ps = VanillaPredictiveSampler(model=model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
+    nsamples = 100
+    stdev = 0.1
+    ps = VanillaPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
 
     N = 10
-    ctrl_model = model.replace(
-        opt=model.opt.replace(
-            timestep=0.03,
+    controller = PredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
+    # jit_compute = jit(
+    #     shard_map(
+    #         lambda key, x_meas, us_guess: controller.compute_with_us_star(
+    #             PredictiveSamplingControllerParams(key=key, x=x_meas, us_guess=us_guess)
+    #         ),
+    #         mesh=mesh,
+    #         in_specs=(P(), P(), P('i', None)),
+    #         out_specs=(P(), P('i', None)),
+    #         check_rep=False,
+    #     )
+    # )  # [DEBUG]
+    jit_compute = jit(
+        lambda key, x_meas, us_guess: controller.compute_with_us_star(
+            PredictiveSamplingControllerParams(key=key, x=x_meas, us_guess=us_guess)
         )
     )
-    controller = PredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
-
     print("Controller created! Simulating...")
 
     # simulating forward
     key = jax.random.PRNGKey(1234)
     # x0 = jnp.array([0.14021026, 0.04142465, 0.99314054, -0.00491294, -0.00487147,  0.81353587, -0.00491293, -0.00487143, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
-    x0 = 2.0 * (jax.random.uniform(key, shape=(model.nq + model.nv,)) - 0.5)
-    T = 3.0
-    num_steps = int(T / model.opt.timestep)
-    data, xs, us, times = simulate_predictive_sampling_controller(
-        model, controller, x0, num_steps, N, seed=1337, physics_steps_per_control_step=2
-    )  # just testing warm starting the sim
-    print(np.mean(times[1:]))
+    x0 = 2.0 * (jax.random.uniform(key, shape=(ctrl_model.nq + ctrl_model.nv,)) - 0.5)
+    # T = 3.0
+    # num_steps = int(T / model.opt.timestep)
+    # data, xs, us, times = simulate_predictive_sampling_controller(
+    #     model, controller, x0, num_steps, N, seed=1337, physics_steps_per_control_step=2
+    # )  # just testing warm starting the sim
+    # print(np.mean(times[1:]))
 
     # post-hoc visualization
-    i = 0
     mj_data = mujoco.MjData(mj_model)
-    mj_data.qpos[:] = x0[: model.nq]
-    mj_data.qvel[:] = x0[model.nq :]
-    dt = model.opt.timestep
+    mj_data.qpos[:] = x0[: mj_model.nq]
+    mj_data.qvel[:] = x0[mj_model.nq :]
+    dt = mj_model.opt.timestep
+    us_guess = jnp.zeros((N, mj_model.nu))
+    # us_guess = jax.device_put(jnp.zeros((N, mj_model.nu)), NamedSharding(mesh, P('i', None)))  # [DEBUG]
+    compiled = False
+    num_phys_steps_per_control_step = 15
     with mujoco.viewer.launch_passive(mj_model, mj_data) as viewer:
         while viewer.is_running():
-            start = time.time()
-            ui = us[i, :]
-            mj_data.ctrl[:] = ui
-            if i <= int(T / dt):
-                mujoco.mj_step(mj_model, mj_data)
-                # print(f"t: {i * dt:.2f} | qpos: {mj_data.qpos}")
-                # print(f"t: {i * dt:.2f} | cost: {mj_data.qpos @ mj_data.qpos + mj_data.qvel @ mj_data.qvel:.2f}")
-                viewer.sync()
-                i += 1
-                elapsed = time.time() - start
-                if elapsed < mj_model.opt.timestep:
-                    time.sleep(5 * mj_model.opt.timestep)
+            while True:
+                if not compiled:
+                    print("compiling jit_compute...")
+                    compiled = True
+                start = time.time()
+                x_meas = jnp.concatenate((mj_data.qpos, mj_data.qvel))
+                u, _us_guess = jit_compute(key, x_meas, us_guess)
+                us_guess = np.concatenate((_us_guess[1:, :], np.zeros((1, mj_model.nu))))
+                mj_data.ctrl[:] = u
+                print(time.time() - start)
+                for _ in range(num_phys_steps_per_control_step):
+                    start = time.time()
+                    mujoco.mj_step(mj_model, mj_data)
+                    viewer.sync()
+                    elapsed = time.time() - start
+                    if elapsed < mj_model.opt.timestep:
+                        time.sleep(mj_model.opt.timestep)
+                key = jax.random.split(key)[0]
+
+        # start = time.time()
+        # ui = us[i, :]
+        # mj_data.ctrl[:] = ui
+        # if i <= int(T / dt):
+        #     mujoco.mj_step(mj_model, mj_data)
+        #     # print(f"t: {i * dt:.2f} | qpos: {mj_data.qpos}")
+        #     # print(f"t: {i * dt:.2f} | cost: {mj_data.qpos @ mj_data.qpos + mj_data.qvel @ mj_data.qvel:.2f}")
+        #     viewer.sync()
+        #     i += 1
+        #     elapsed = time.time() - start
+        #     if elapsed < mj_model.opt.timestep:
+        #         time.sleep(mj_model.opt.timestep)
 
     breakpoint()

From 0da8ce2a6ca0d01e50860023778dba450139c45d Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sat, 9 Dec 2023 14:21:58 -0800
Subject: [PATCH 08/15] reorganize examples

---
 examples/control/predictive_sampling.py      | 96 ++++++++++++++++++++
 examples/{ => io}/convex_decomposition.py    |  0
 examples/{ => io}/load_from_file.py          |  0
 examples/{ => sim}/interactive_simulation.py |  0
 4 files changed, 96 insertions(+)
 create mode 100644 examples/control/predictive_sampling.py
 rename examples/{ => io}/convex_decomposition.py (100%)
 rename examples/{ => io}/load_from_file.py (100%)
 rename examples/{ => sim}/interactive_simulation.py (100%)

diff --git a/examples/control/predictive_sampling.py b/examples/control/predictive_sampling.py
new file mode 100644
index 00000000..82fe4a0e
--- /dev/null
+++ b/examples/control/predictive_sampling.py
@@ -0,0 +1,96 @@
+import time
+
+import jax
+import jax.numpy as jnp
+import mujoco
+import mujoco.viewer
+import numpy as np
+from jax import jit
+
+from ambersim.control.predictive_control import PredictiveSamplingController, PredictiveSamplingControllerParams
+from ambersim.trajopt.cost import StaticGoalQuadraticCost
+from ambersim.trajopt.shooting import VanillaPredictiveSampler
+from ambersim.utils.io_utils import load_mj_model_from_file, mj_to_mjx_model_and_data
+
+# ########## #
+# PARAMETERS #
+# ########## #
+
+dt = 0.001  # rate of reality
+num_phys_steps_per_control_step = 15  # dt * npspcs = rate of control
+
+w_pos = 10.0
+w_vel = 0.01
+w_ctrl = 0.001
+q_goal = jnp.array([0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 1.0, 0.8, 0.5, 0.5])
+
+nsamples = 100  # number of samples to draw for predictive sampling
+stdev = 0.1  # standard deviation of control parameters to draw
+N = 10  # number of time steps to predict
+
+key = jax.random.PRNGKey(1234)
+
+# ########## #
+# SIMULATION #
+# ########## #
+
+# loading model
+mj_model = load_mj_model_from_file("models/allegro_hand/right_hand.xml")
+mj_model.opt.timestep = dt
+
+# defining the predictive controller
+ctrl_model, _ = mj_to_mjx_model_and_data(mj_model)
+ctrl_model = ctrl_model.replace(
+    opt=ctrl_model.opt.replace(
+        timestep=num_phys_steps_per_control_step * dt,  # dt for each step in the controller's internal model
+        iterations=1,  # number of Newton steps to take during solve
+        ls_iterations=1,  # number of line search iterations along step direction
+        integrator=0,  # Euler semi-implicit integration
+        solver=2,  # Newton solver
+    )
+)
+cost_function = StaticGoalQuadraticCost(
+    Q=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
+    Qf=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
+    R=w_ctrl * jnp.eye(ctrl_model.nu),
+    xg=jnp.concatenate((q_goal, jnp.zeros(16))),
+)
+ps = VanillaPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
+controller = PredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
+jit_compute = jit(
+    lambda key, x_meas, us_guess: controller.compute_with_us_star(
+        PredictiveSamplingControllerParams(key=key, x=x_meas, us_guess=us_guess)
+    )
+)
+print("Controller created! Simulating...")
+
+# simulating forward
+x0 = 2.0 * (jax.random.uniform(key, shape=(ctrl_model.nq + ctrl_model.nv,)) - 0.5)  # random initial state
+
+mj_data = mujoco.MjData(mj_model)
+mj_data.qpos[:] = x0[: mj_model.nq]
+mj_data.qvel[:] = x0[mj_model.nq :]
+
+us_guess = jnp.zeros((N, mj_model.nu))
+
+print("Compiling jit_compute...")
+jit_compute(key, x0, us_guess)
+print("Compiled! Beginning simulation...")
+
+with mujoco.viewer.launch_passive(mj_model, mj_data) as viewer:
+    while viewer.is_running():
+        while True:
+            start = time.time()
+            x_meas = jnp.concatenate((mj_data.qpos, mj_data.qvel))
+            u, _us_guess = jit_compute(key, x_meas, us_guess)
+            us_guess = np.concatenate((_us_guess[1:, :], np.zeros((1, mj_model.nu))))
+            mj_data.ctrl[:] = u
+            print(f"Controller delay: {time.time() - start}")
+            for _ in range(num_phys_steps_per_control_step):
+                start = time.time()
+                mujoco.mj_step(mj_model, mj_data)
+                viewer.sync()
+                elapsed = time.time() - start
+                if elapsed < mj_model.opt.timestep:
+                    time.sleep(mj_model.opt.timestep - elapsed)
+            key = jax.random.split(key)[0]
diff --git a/examples/convex_decomposition.py b/examples/io/convex_decomposition.py
similarity index 100%
rename from examples/convex_decomposition.py
rename to examples/io/convex_decomposition.py
diff --git a/examples/load_from_file.py b/examples/io/load_from_file.py
similarity index 100%
rename from examples/load_from_file.py
rename to examples/io/load_from_file.py
diff --git a/examples/interactive_simulation.py b/examples/sim/interactive_simulation.py
similarity index 100%
rename from examples/interactive_simulation.py
rename to examples/sim/interactive_simulation.py

From d3c2b7c098d040d67a7c8032e6d67f3f4f3a85a1 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sat, 9 Dec 2023 14:59:03 -0800
Subject: [PATCH 09/15] simplify a function in predictive_control

---
 ambersim/control/predictive_control.py | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/ambersim/control/predictive_control.py b/ambersim/control/predictive_control.py
index c662c2c1..f92fea14 100644
--- a/ambersim/control/predictive_control.py
+++ b/ambersim/control/predictive_control.py
@@ -64,14 +64,7 @@ def compute(self, ctrl_params: PredictiveSamplingControllerParams) -> jax.Array:
         Returns:
             u (shape=(nu,)): The control input.
         """
-        to_params = VanillaPredictiveSamplerParams(
-            key=ctrl_params.key,
-            x0=ctrl_params.x,
-            us_guess=ctrl_params.us_guess,
-        )
-        _, us_star = self.trajectory_optimizer.optimize(to_params)
-        u = us_star[0, :]  # (nu,)
-        return u
+        return self.compute_with_us_star(ctrl_params)[0]
 
     def compute_with_us_star(self, ctrl_params: PredictiveSamplingControllerParams) -> Tuple[jax.Array, jax.Array]:
         """Computes a control input using forward prediction + the optimal sequence of guesses.

From ccb787571aca30ece893ecfa4ddcfaa0b6e295e3 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sat, 9 Dec 2023 17:09:51 -0800
Subject: [PATCH 10/15] save .stl files also in package

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 64bd86e4..0fb40aa3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,7 +59,7 @@ install-mujoco-from-src = "ambersim._scripts._install_shim:entrypoint"
 include = ["ambersim*"]
 
 [tool.setuptools.package-data]
-"*" = ["*.obj", "*.urdf", "*.xml", "install.sh", "install_mj_source.sh"]
+"*" = ["*.obj", "*.stl", "*.urdf", "*.xml", "install.sh", "install_mj_source.sh"]
 
 [tool.black]
 line-length = 120

From 26bbaeae742fb1148fce9b236a992b9a9b4b79f1 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sat, 9 Dec 2023 22:10:54 -0800
Subject: [PATCH 11/15] add PD predictive sampler

---
 ambersim/control/predictive_control.py        |  81 +++++-
 .../{left_hand.xml => left_hand_motor.xml}    |   0
 .../allegro_hand/left_hand_position.xml       | 259 +++++++++++++++++
 .../{right_hand.xml => right_hand_motor.xml}  |   0
 .../allegro_hand/right_hand_position.xml      | 260 ++++++++++++++++++
 ambersim/sim/simulation.py                    |  15 +-
 ambersim/trajopt/shooting.py                  | 165 ++++++++++-
 examples/control/predictive_sampling.py       |   9 +-
 tests/trajopt/test_predictive_sampler.py      |  67 ++++-
 9 files changed, 820 insertions(+), 36 deletions(-)
 rename ambersim/models/allegro_hand/{left_hand.xml => left_hand_motor.xml} (100%)
 create mode 100644 ambersim/models/allegro_hand/left_hand_position.xml
 rename ambersim/models/allegro_hand/{right_hand.xml => right_hand_motor.xml} (100%)
 create mode 100644 ambersim/models/allegro_hand/right_hand_position.xml

diff --git a/ambersim/control/predictive_control.py b/ambersim/control/predictive_control.py
index f92fea14..e75eb1ff 100644
--- a/ambersim/control/predictive_control.py
+++ b/ambersim/control/predictive_control.py
@@ -7,7 +7,7 @@
 
 from ambersim.control.base import Controller, ControllerParams
 from ambersim.trajopt.base import TrajectoryOptimizer
-from ambersim.trajopt.shooting import VanillaPredictiveSampler, VanillaPredictiveSamplerParams
+from ambersim.trajopt.shooting import PDPredictiveSamplerParams, PredictiveSampler, VanillaPredictiveSamplerParams
 
 # ########### #
 # GENERIC API #
@@ -37,25 +37,25 @@ def compute(self, ctrl_params: PredictiveControllerParams) -> jax.Array:
 
 
 @struct.dataclass
-class PredictiveSamplingControllerParams(PredictiveControllerParams):
-    """Predictive sampling controller params."""
+class VanillaPredictiveSamplingControllerParams(PredictiveControllerParams):
+    """Vanilla predictive sampling controller params."""
 
     key: jax.Array  # random key for sampling
     x: jax.Array  # shape=(nq + nv,) current state
-    us_guess: jax.Array  # shape=(N, nu) current guess
+    guess: jax.Array  # shape=(N, nu) current guess
 
 
 @struct.dataclass
-class PredictiveSamplingController(PredictiveController):
-    """Predictive sampling controller."""
+class VanillaPredictiveSamplingController(PredictiveController):
+    """Vanilla predictive sampling controller."""
 
     def __post_init__(self) -> None:
         """Post-initialization check."""
         assert isinstance(
-            self.trajectory_optimizer, VanillaPredictiveSampler
-        ), "trajectory_optimizer must be a VanillaPredictiveSampler!"  # TODO(ahl): this is too restrictive
+            self.trajectory_optimizer, PredictiveSampler
+        ), "trajectory_optimizer must be a PredictiveSampler!"
 
-    def compute(self, ctrl_params: PredictiveSamplingControllerParams) -> jax.Array:
+    def compute(self, ctrl_params: VanillaPredictiveSamplingControllerParams) -> jax.Array:
         """Computes a control input using forward prediction.
 
         Args:
@@ -66,7 +66,9 @@ def compute(self, ctrl_params: PredictiveSamplingControllerParams) -> jax.Array:
         """
         return self.compute_with_us_star(ctrl_params)[0]
 
-    def compute_with_us_star(self, ctrl_params: PredictiveSamplingControllerParams) -> Tuple[jax.Array, jax.Array]:
+    def compute_with_us_star(
+        self, ctrl_params: VanillaPredictiveSamplingControllerParams
+    ) -> Tuple[jax.Array, jax.Array]:
         """Computes a control input using forward prediction + the optimal sequence of guesses.
 
         This is needed in practice because the current optimal sequence is used to warm start the sampling distribution
@@ -82,7 +84,64 @@ def compute_with_us_star(self, ctrl_params: PredictiveSamplingControllerParams)
         to_params = VanillaPredictiveSamplerParams(
             key=ctrl_params.key,
             x0=ctrl_params.x,
-            us_guess=ctrl_params.us_guess,
+            guess=ctrl_params.guess,
+        )
+        xs_star, us_star = self.trajectory_optimizer.optimize(to_params)
+        u = us_star[0, :]
+        return u, us_star
+
+
+@struct.dataclass
+class PDPredictiveSamplingControllerParams(PredictiveControllerParams):
+    """PD predictive sampling controller params."""
+
+    key: jax.Array  # random key for sampling
+    x: jax.Array  # shape=(nq + nv,) current state
+    guess: jax.Array  # shape=(N, nq) current guess
+    kp: float  # proportional gain
+    kd: float  # derivative gain
+
+
+@struct.dataclass
+class PDPredictiveSamplingController(PredictiveController):
+    """PD predictive sampling controller."""
+
+    def __post_init__(self) -> None:
+        """Post-initialization check."""
+        assert isinstance(
+            self.trajectory_optimizer, PredictiveSampler
+        ), "trajectory_optimizer must be a PredictiveSampler!"
+
+    def compute(self, ctrl_params: PDPredictiveSamplingControllerParams) -> jax.Array:
+        """Computes a control input using forward prediction.
+
+        Args:
+            ctrl_params: Inputs into the controller.
+
+        Returns:
+            u (shape=(nu,)): The control input.
+        """
+        return self.compute_with_us_star(ctrl_params)[0]
+
+    def compute_with_us_star(self, ctrl_params: PDPredictiveSamplingControllerParams) -> Tuple[jax.Array, jax.Array]:
+        """Computes a control input using forward prediction + the optimal sequence of guesses.
+
+        This is needed in practice because the current optimal sequence is used to warm start the sampling distribution
+        for the next call of the controller.
+
+        Args:
+            ctrl_params: Inputs into the controller.
+
+        Returns:
+            u (shape=(nu,)): The control input.
+            us_star (shape=(N, nu)): The optimal control sequence.
+        """
+        to_params = PDPredictiveSamplerParams(
+            key=ctrl_params.key,
+            x0=ctrl_params.x,
+            guess=ctrl_params.guess,
+            kp=ctrl_params.kp,
+            kd=ctrl_params.kd,
         )
         xs_star, us_star = self.trajectory_optimizer.optimize(to_params)
         u = us_star[0, :]
diff --git a/ambersim/models/allegro_hand/left_hand.xml b/ambersim/models/allegro_hand/left_hand_motor.xml
similarity index 100%
rename from ambersim/models/allegro_hand/left_hand.xml
rename to ambersim/models/allegro_hand/left_hand_motor.xml
diff --git a/ambersim/models/allegro_hand/left_hand_position.xml b/ambersim/models/allegro_hand/left_hand_position.xml
new file mode 100644
index 00000000..0c778d13
--- /dev/null
+++ b/ambersim/models/allegro_hand/left_hand_position.xml
@@ -0,0 +1,259 @@
+<mujoco model="allegro_left">
+  <compiler angle="radian" meshdir="assets" autolimits="true"/>
+
+  <option cone="pyramidal" impratio="10"/>
+
+  <default>
+    <default class="allegro_left">
+      <joint axis="0 1 0" damping=".1"/>
+      <position kp="1"/>
+      <geom density="800"/>
+
+      <default class="visual">
+        <geom type="mesh" contype="0" conaffinity="0" group="2" material="black"/>
+        <default class="palm_visual">
+          <geom mesh="base_link"/>
+        </default>
+        <default class="base_visual">
+          <geom mesh="link_0.0"/>
+        </default>
+        <default class="proximal_visual">
+          <geom mesh="link_1.0"/>
+        </default>
+        <default class="medial_visual">
+          <geom mesh="link_2.0"/>
+        </default>
+        <default class="distal_visual">
+          <geom mesh="link_3.0"/>
+        </default>
+        <default class="fingertip_visual">
+          <geom pos="0 0 0.0267" material="white" mesh="link_3.0_tip"/>
+        </default>
+        <default class="thumbtip_visual">
+          <geom pos="0 0 0.0423" material="white" mesh="link_15.0_tip"/>
+        </default>
+      </default>
+
+      <default class="collision">
+        <geom group="3" type="box" mass="0"/>
+        <default class="palm_collision">
+          <geom size="0.0204 0.0565 0.0475" pos="-0.0093 0 -0.0475"/>
+        </default>
+        <default class="base_collision">
+          <geom size="0.0098 0.01375 0.0082" pos="0 0 0.0082"/>
+          <default class="thumb_base_collision">
+            <geom size="0.0179 0.017 0.02275" pos="-0.0179 0.009 0.0145"/>
+          </default>
+        </default>
+        <default class="proximal_collision">
+          <geom size="0.0098 0.01375 0.027" pos="0 0 0.027"/>
+          <default class="thumb_proximal_collision">
+            <geom size="0.0098 0.01375 0.00885" pos="0 0 0.00885"/>
+          </default>
+        </default>
+        <default class="medial_collision">
+          <geom size="0.0098 0.01375 0.0192" pos="0 0 0.0192"/>
+          <default class="thumb_medial_collision">
+            <geom size="0.0098 0.01375 0.0257" pos="0 0 0.0257"/>
+          </default>
+        </default>
+        <default class="distal_collision">
+          <geom size="0.0098 0.01375 0.008" pos="0 0 0.008"/>
+          <default class="thumb_distal_collision">
+            <geom size="0.0098 0.01375 0.0157" pos="0 0 0.0157"/>
+          </default>
+        </default>
+        <default class="fingertip_collision">
+          <geom type="capsule" size="0.012 0.01" pos="0 0 0.019"/>
+          <default class="thumbtip_collision">
+            <geom type="capsule" size="0.012 0.008" pos="0 0 0.035"/>
+          </default>
+        </default>
+      </default>
+
+      <default class="base">
+        <joint axis="0 0 1" range="-0.47 0.47"/>
+        <position ctrlrange="-0.47 0.47"/>
+      </default>
+      <default class="proximal">
+        <joint range="-0.196 1.61"/>
+        <position ctrlrange="-0.196 1.61"/>
+      </default>
+      <default class="medial">
+        <joint range="-0.174 1.709"/>
+        <position ctrlrange="-0.174 1.709"/>
+      </default>
+      <default class="distal">
+        <joint range="-0.227 1.618"/>
+        <position ctrlrange="-0.227 1.618"/>
+      </default>
+      <default class="thumb_base">
+        <joint axis="1 0 0" range="0.263 1.396"/>
+        <position ctrlrange="0.263 1.396"/>
+      </default>
+      <default class="thumb_proximal">
+        <joint axis="0 0 -1" range="-0.105 1.163"/>
+        <position ctrlrange="-0.105 1.163"/>
+      </default>
+      <default class="thumb_medial">
+        <joint range="-0.189 1.644"/>
+        <position ctrlrange="-0.189 1.644"/>
+      </default>
+      <default class="thumb_distal">
+        <joint range="-0.162 1.719"/>
+        <position ctrlrange="-0.162 1.719"/>
+      </default>
+    </default>
+  </default>
+
+  <asset>
+    <material name="black" rgba="0.2 0.2 0.2 1"/>
+    <material name="white" rgba="0.9 0.9 0.9 1"/>
+
+    <mesh file="base_link_left.stl"/>
+    <mesh file="link_0.0.stl"/>
+    <mesh file="link_1.0.stl"/>
+    <mesh file="link_2.0.stl"/>
+    <mesh file="link_3.0.stl"/>
+    <mesh file="link_3.0_tip.stl"/>
+    <mesh file="link_12.0_left.stl"/>
+    <mesh file="link_13.0.stl"/>
+    <mesh file="link_14.0.stl"/>
+    <mesh file="link_15.0.stl"/>
+    <mesh file="link_15.0_tip.stl"/>
+  </asset>
+
+  <worldbody>
+    <body name="palm" quat="0 1 0 1" childclass="allegro_left">
+      <geom quat="1 -1 0 0" class="palm_visual" mesh="base_link_left"/>
+      <geom class="palm_collision"/>
+      <!-- Ring finger -->
+      <body name="rf_base" pos="0 0.0435 -0.001542" quat="0.999048 -0.0436194 0 0">
+        <joint name="rfj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="rf_proximal" pos="0 0 0.0164">
+          <joint name="rfj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="rf_medial" pos="0 0 0.054">
+            <joint name="rfj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="rf_distal" pos="0 0 0.0384">
+              <joint name="rfj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="rf_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Middle finger -->
+      <body name="mf_base" pos="0 0 0.0007">
+        <joint name="mfj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="mf_proximal" pos="0 0 0.0164">
+          <joint name="mfj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="mf_medial" pos="0 0 0.054">
+            <joint name="mfj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="mf_distal" pos="0 0 0.0384">
+              <joint name="mfj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="mf_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- First finger -->
+      <body name="ff_base" pos="0 -0.0435 -0.001542" quat="0.999048 0.0436194 0 0">
+        <joint name="ffj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="ff_proximal" pos="0 0 0.0164">
+          <joint name="ffj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="ff_medial" pos="0 0 0.054">
+            <joint name="ffj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="ff_distal" pos="0 0 0.0384">
+              <joint name="ffj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="ff_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Thumb -->
+      <body name="th_base" pos="-0.0182 -0.019333 -0.045987" quat="0.477714 0.521334 -0.521334 0.477714">
+        <joint name="thj0" class="thumb_base"/>
+        <geom quat="0 1 0 0" class="visual" mesh="link_12.0_left"/>
+        <geom class="thumb_base_collision"/>
+        <body name="th_proximal" pos="-0.027 -0.005 0.0399">
+          <joint name="thj1" class="thumb_proximal"/>
+          <geom class="visual" mesh="link_13.0"/>
+          <geom class="thumb_proximal_collision"/>
+          <body name="th_medial" pos="0 0 0.0177">
+            <joint name="thj2" class="thumb_medial"/>
+            <geom class="visual" mesh="link_14.0"/>
+            <geom class="thumb_medial_collision"/>
+            <body name="th_distal" pos="0 0 0.0514">
+              <joint name="thj3" class="thumb_distal"/>
+              <geom class="visual" mesh="link_15.0"/>
+              <geom class="thumb_distal_collision"/>
+              <body name="th_tip">
+                <geom class="thumbtip_visual"/>
+                <geom class="thumbtip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+    </body>
+  </worldbody>
+
+  <contact>
+    <exclude body1="palm" body2="ff_base"/>
+    <exclude body1="palm" body2="mf_base"/>
+    <exclude body1="palm" body2="rf_base"/>
+    <exclude body1="palm" body2="th_base"/>
+    <exclude body1="palm" body2="th_proximal"/>
+  </contact>
+
+  <actuator>
+    <position name="ffa0" joint="ffj0" class="base"/>
+    <position name="ffa1" joint="ffj1" class="proximal"/>
+    <position name="ffa2" joint="ffj2" class="medial"/>
+    <position name="ffa3" joint="ffj3" class="distal"/>
+    <position name="mfa0" joint="mfj0" class="base"/>
+    <position name="mfa1" joint="mfj1" class="proximal"/>
+    <position name="mfa2" joint="mfj2" class="medial"/>
+    <position name="mfa3" joint="mfj3" class="distal"/>
+    <position name="rfa0" joint="rfj0" class="base"/>
+    <position name="rfa1" joint="rfj1" class="proximal"/>
+    <position name="rfa2" joint="rfj2" class="medial"/>
+    <position name="rfa3" joint="rfj3" class="distal"/>
+    <position name="tha0" joint="thj0" class="thumb_base"/>
+    <position name="tha1" joint="thj1" class="thumb_proximal"/>
+    <position name="tha2" joint="thj2" class="thumb_medial"/>
+    <position name="tha3" joint="thj3" class="thumb_distal"/>
+  </actuator>
+</mujoco>
diff --git a/ambersim/models/allegro_hand/right_hand.xml b/ambersim/models/allegro_hand/right_hand_motor.xml
similarity index 100%
rename from ambersim/models/allegro_hand/right_hand.xml
rename to ambersim/models/allegro_hand/right_hand_motor.xml
diff --git a/ambersim/models/allegro_hand/right_hand_position.xml b/ambersim/models/allegro_hand/right_hand_position.xml
new file mode 100644
index 00000000..1b6bf085
--- /dev/null
+++ b/ambersim/models/allegro_hand/right_hand_position.xml
@@ -0,0 +1,260 @@
+<mujoco model="allegro_right">
+  <compiler angle="radian" meshdir="assets" autolimits="true"/>
+
+  <option cone="pyramidal" impratio="10"/>
+
+  <default>
+    <default class="allegro_right">
+      <joint axis="0 1 0" damping=".1"/>
+      <position kp="1"/>
+      <geom density="800"/>
+
+      <default class="visual">
+        <geom type="mesh" contype="0" conaffinity="0" group="2" material="black"/>
+        <default class="palm_visual">
+          <geom mesh="base_link"/>
+        </default>
+        <default class="base_visual">
+          <geom mesh="link_0.0"/>
+        </default>
+        <default class="proximal_visual">
+          <geom mesh="link_1.0"/>
+        </default>
+        <default class="medial_visual">
+          <geom mesh="link_2.0"/>
+        </default>
+        <default class="distal_visual">
+          <geom mesh="link_3.0"/>
+        </default>
+        <default class="fingertip_visual">
+          <geom pos="0 0 0.0267" material="white" mesh="link_3.0_tip"/>
+        </default>
+        <default class="thumbtip_visual">
+          <geom pos="0 0 0.0423" material="white" mesh="link_15.0_tip"/>
+        </default>
+      </default>
+
+      <default class="collision">
+        <geom group="3" type="box" mass="0"/>
+        <default class="palm_collision">
+          <geom size="0.0204 0.0565 0.0475" pos="-0.0093 0 -0.0475"/>
+        </default>
+        <default class="base_collision">
+          <geom size="0.0098 0.01375 0.0082" pos="0 0 0.0082"/>
+          <default class="thumb_base_collision">
+            <geom size="0.0179 0.017 0.02275" pos="-0.0179 0.009 0.0145"/>
+          </default>
+        </default>
+        <default class="proximal_collision">
+          <geom size="0.0098 0.01375 0.027" pos="0 0 0.027"/>
+          <default class="thumb_proximal_collision">
+            <geom size="0.0098 0.01375 0.00885" pos="0 0 0.00885"/>
+          </default>
+        </default>
+        <default class="medial_collision">
+          <geom size="0.0098 0.01375 0.0192" pos="0 0 0.0192"/>
+          <default class="thumb_medial_collision">
+            <geom size="0.0098 0.01375 0.0257" pos="0 0 0.0257"/>
+          </default>
+        </default>
+        <default class="distal_collision">
+          <geom size="0.0098 0.01375 0.008" pos="0 0 0.008"/>
+          <default class="thumb_distal_collision">
+            <geom size="0.0098 0.01375 0.0157" pos="0 0 0.0157"/>
+          </default>
+        </default>
+        <default class="fingertip_collision">
+          <geom type="capsule" size="0.012 0.01" pos="0 0 0.019"/>
+          <default class="thumbtip_collision">
+            <geom type="capsule" size="0.012 0.008" pos="0 0 0.035"/>
+          </default>
+        </default>
+      </default>
+
+      <default class="base">
+        <joint axis="0 0 1" range="-0.47 0.47"/>
+        <position ctrlrange="-0.47 0.47"/>
+      </default>
+      <default class="proximal">
+        <joint range="-0.196 1.61"/>
+        <position ctrlrange="-0.196 1.61"/>
+      </default>
+      <default class="medial">
+        <joint range="-0.174 1.709"/>
+        <position ctrlrange="-0.174 1.709"/>
+      </default>
+      <default class="distal">
+        <joint range="-0.227 1.618"/>
+        <position ctrlrange="-0.227 1.618"/>
+      </default>
+      <default class="thumb_base">
+        <joint axis="-1 0 0" range="0.263 1.396"/>
+        <position ctrlrange="0.263 1.396"/>
+      </default>
+      <default class="thumb_proximal">
+        <joint axis="0 0 1" range="-0.105 1.163"/>
+        <position ctrlrange="-0.105 1.163"/>
+      </default>
+      <default class="thumb_medial">
+        <joint range="-0.189 1.644"/>
+        <position ctrlrange="-0.189 1.644"/>
+      </default>
+      <default class="thumb_distal">
+        <joint range="-0.162 1.719"/>
+        <position ctrlrange="-0.162 1.719"/>
+      </default>
+    </default>
+  </default>
+
+  <asset>
+    <material name="black" rgba="0.2 0.2 0.2 1"/>
+    <material name="white" rgba="0.9 0.9 0.9 1"/>
+
+    <mesh file="base_link.stl"/>
+    <mesh file="link_0.0.stl"/>
+    <mesh file="link_1.0.stl"/>
+    <mesh file="link_2.0.stl"/>
+    <mesh file="link_3.0.stl"/>
+    <mesh file="link_3.0_tip.stl"/>
+    <mesh file="link_12.0_right.stl"/>
+    <mesh file="link_13.0.stl"/>
+    <mesh file="link_14.0.stl"/>
+    <mesh file="link_15.0.stl"/>
+    <mesh file="link_15.0_tip.stl"/>
+  </asset>
+
+  <worldbody>
+    <body name="palm" quat="0 1 0 1" childclass="allegro_right">
+      <!-- <inertial mass="0.4154" pos="0 0 0.0475" diaginertia="1e-4 1e-4 1e-4"/> -->
+      <geom class="palm_visual" mesh="base_link"/>
+      <geom class="palm_collision"/>
+      <!-- First finger -->
+      <body name="ff_base" pos="0 0.0435 -0.001542" quat="0.999048 -0.0436194 0 0">
+        <joint name="ffj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="ff_proximal" pos="0 0 0.0164">
+          <joint name="ffj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="ff_medial" pos="0 0 0.054">
+            <joint name="ffj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="ff_distal" pos="0 0 0.0384">
+              <joint name="ffj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="ff_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Middle finger -->
+      <body name="mf_base" pos="0 0 0.0007">
+        <joint name="mfj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="mf_proximal" pos="0 0 0.0164">
+          <joint name="mfj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="mf_medial" pos="0 0 0.054">
+            <joint name="mfj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="mf_distal" pos="0 0 0.0384">
+              <joint name="mfj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="mf_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Ring finger -->
+      <body name="rf_base" pos="0 -0.0435 -0.001542" quat="0.999048 0.0436194 0 0">
+        <joint name="rfj0" class="base"/>
+        <geom class="base_visual"/>
+        <geom class="base_collision"/>
+        <body name="rf_proximal" pos="0 0 0.0164">
+          <joint name="rfj1" class="proximal"/>
+          <geom class="proximal_visual"/>
+          <geom class="proximal_collision"/>
+          <body name="rf_medial" pos="0 0 0.054">
+            <joint name="rfj2" class="medial"/>
+            <geom class="medial_visual"/>
+            <geom class="medial_collision"/>
+            <body name="rf_distal" pos="0 0 0.0384">
+              <joint name="rfj3" class="distal"/>
+              <geom class="distal_visual"/>
+              <geom class="distal_collision"/>
+              <body name="rf_tip">
+                <geom class="fingertip_visual"/>
+                <geom class="fingertip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+      <!-- Thumb -->
+      <body name="th_base" pos="-0.0182 0.019333 -0.045987" quat="0.477714 -0.521334 -0.521334 -0.477714">
+        <joint name="thj0" class="thumb_base"/>
+        <geom class="visual" mesh="link_12.0_right"/>
+        <geom class="thumb_base_collision"/>
+        <body name="th_proximal" pos="-0.027 0.005 0.0399">
+          <joint name="thj1" class="thumb_proximal"/>
+          <geom class="visual" mesh="link_13.0"/>
+          <geom class="thumb_proximal_collision"/>
+          <body name="th_medial" pos="0 0 0.0177">
+            <joint name="thj2" class="thumb_medial"/>
+            <geom class="visual" mesh="link_14.0"/>
+            <geom class="thumb_medial_collision"/>
+            <body name="th_distal" pos="0 0 0.0514">
+              <joint name="thj3" class="thumb_distal"/>
+              <geom class="visual" mesh="link_15.0"/>
+              <geom class="thumb_distal_collision"/>
+              <body name="th_tip">
+                <geom class="thumbtip_visual"/>
+                <geom class="thumbtip_collision"/>
+              </body>
+            </body>
+          </body>
+        </body>
+      </body>
+    </body>
+  </worldbody>
+
+  <contact>
+    <exclude body1="palm" body2="ff_base"/>
+    <exclude body1="palm" body2="mf_base"/>
+    <exclude body1="palm" body2="rf_base"/>
+    <exclude body1="palm" body2="th_base"/>
+    <exclude body1="palm" body2="th_proximal"/>
+  </contact>
+
+  <actuator>
+    <position name="ffa0" joint="ffj0" class="base"/>
+    <position name="ffa1" joint="ffj1" class="proximal"/>
+    <position name="ffa2" joint="ffj2" class="medial"/>
+    <position name="ffa3" joint="ffj3" class="distal"/>
+    <position name="mfa0" joint="mfj0" class="base"/>
+    <position name="mfa1" joint="mfj1" class="proximal"/>
+    <position name="mfa2" joint="mfj2" class="medial"/>
+    <position name="mfa3" joint="mfj3" class="distal"/>
+    <position name="rfa0" joint="rfj0" class="base"/>
+    <position name="rfa1" joint="rfj1" class="proximal"/>
+    <position name="rfa2" joint="rfj2" class="medial"/>
+    <position name="rfa3" joint="rfj3" class="distal"/>
+    <position name="tha0" joint="thj0" class="thumb_base"/>
+    <position name="tha1" joint="thj1" class="thumb_proximal"/>
+    <position name="tha2" joint="thj2" class="thumb_medial"/>
+    <position name="tha3" joint="thj3" class="thumb_distal"/>
+  </actuator>
+</mujoco>
diff --git a/ambersim/sim/simulation.py b/ambersim/sim/simulation.py
index de356437..d3da7dae 100644
--- a/ambersim/sim/simulation.py
+++ b/ambersim/sim/simulation.py
@@ -3,14 +3,17 @@
 from jax import jit, lax
 from mujoco import mjx
 
-from ambersim.control.predictive_control import PredictiveSamplingController, PredictiveSamplingControllerParams
+from ambersim.control.predictive_control import (
+    VanillaPredictiveSamplingController,
+    VanillaPredictiveSamplingControllerParams,
+)
 
 """This file contains simulation utils for various controllers."""
 
 
 def simulate_predictive_sampling_controller(
     model: mjx.Model,
-    controller: PredictiveSamplingController,
+    controller: VanillaPredictiveSamplingController,
     x0: jax.Array,
     num_steps: int,
     N: int,
@@ -47,7 +50,7 @@ def simulate_predictive_sampling_controller(
 
     jit_compute = jit(
         lambda key, x_meas, us_guess: controller.compute_with_us_star(
-            PredictiveSamplingControllerParams(key=key, x=x_meas, us_guess=us_guess)
+            VanillaPredictiveSamplingControllerParams(key=key, x=x_meas, guess=us_guess)
         )
     )
     jit_step = jit(lambda data, u: mjx.step(model, data.replace(ctrl=u)))
@@ -135,11 +138,11 @@ def simulate_predictive_sampling_controller(
     ps = VanillaPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
 
     N = 10
-    controller = PredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
+    controller = VanillaPredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
     # jit_compute = jit(
     #     shard_map(
     #         lambda key, x_meas, us_guess: controller.compute_with_us_star(
-    #             PredictiveSamplingControllerParams(key=key, x=x_meas, us_guess=us_guess)
+    #             VanillaPredictiveSamplingControllerParams(key=key, x=x_meas, guess=us_guess)
     #         ),
     #         mesh=mesh,
     #         in_specs=(P(), P(), P('i', None)),
@@ -149,7 +152,7 @@ def simulate_predictive_sampling_controller(
     # )  # [DEBUG]
     jit_compute = jit(
         lambda key, x_meas, us_guess: controller.compute_with_us_star(
-            PredictiveSamplingControllerParams(key=key, x=x_meas, us_guess=us_guess)
+            VanillaPredictiveSamplingControllerParams(key=key, x=x_meas, guess=us_guess)
         )
     )
     print("Controller created! Simulating...")
diff --git a/ambersim/trajopt/shooting.py b/ambersim/trajopt/shooting.py
index 0fd4abc4..06134b10 100644
--- a/ambersim/trajopt/shooting.py
+++ b/ambersim/trajopt/shooting.py
@@ -24,8 +24,8 @@ def shoot(m: mjx.Model, x0: jax.Array, us: jax.Array) -> jax.Array:
 
     Args:
         m: The model.
-        x0: The initial state.
-        us: The control inputs.
+        x0 (shape=(nx,)): The initial state.
+        us (shape=(N, nu)): The control inputs.
 
     Returns:
         xs (shape=(N + 1, nq + nv)): The state trajectory.
@@ -48,6 +48,46 @@ def scan_fn(d, u):
     return xs
 
 
+def shoot_pd(m: mjx.Model, x0: jax.Array, xgs: jax.Array, kp: float, kd: float) -> jax.Array:
+    """Utility function that shoots a model forward given a sequence of goal states + PD gains.
+
+    Args:
+        m: The model.
+        x0 (shape=(nx,)): The initial state.
+        xgs (shape=(N, nx)): The goal trajectory.
+        kp: The proportional gain.
+        kd: The derivative gain.
+
+    Returns:
+        xs (shape=(N + 1, nq + nv)): The state trajectory.
+        us (shape=(N, nu)): The control inputs.
+    """
+    # initializing the data
+    d = mjx.make_data(m)
+    d = d.replace(qpos=x0[: m.nq], qvel=x0[m.nq :])  # setting the initial state.
+    d = mjx.forward(m, d)  # setting other internal states like acceleration without integrating
+
+    def scan_fn(d, xg):
+        """Integrates the model forward one step given the control input u."""
+        # applying PD controller
+        qg = xg[: m.nq]
+        vg = xg[m.nq :]
+        u = -kp * (d.qpos - qg) - kd * (d.qvel - vg)
+
+        d = d.replace(ctrl=u)
+        d = step(m, d)
+        x = jnp.concatenate((d.qpos, d.qvel))  # (nq + nv,)
+        xu = jnp.concatenate((x, u))
+        return d, xu
+
+    # scan over the control inputs to get the trajectory.
+    _, _xus = lax.scan(scan_fn, d, xgs, length=xgs.shape[0])
+    _xs = _xus[:, : m.nq + m.nv]
+    xs = jnp.concatenate((x0[None, :], _xs), axis=0)  # (N + 1, nq + nv)
+    us = _xus[:, m.nq + m.nv :]
+    return xs, us
+
+
 # ################ #
 # SHOOTING METHODS #
 # ################ #
@@ -61,16 +101,18 @@ class ShootingParams(TrajectoryOptimizerParams):
 
     # inputs into the algorithm
     x0: jax.Array  # shape=(nq + nv,) or (?)
-    us_guess: jax.Array  # shape=(N, nu) or (?)
+    guess: jax.Array  # shape=(N, n?) or (?)
 
     @property
     def N(self) -> int:
         """The number of time steps.
 
-        By default, we assume us_guess represents the ZOH control inputs. However, in the case that it is actually an
+        By default, we assume guess represents the ZOH control inputs. However, in the case that it is actually an
         alternate parameterization, we may need to compute N some other way, which requires overwriting this method.
+
+        In the case of PD controllers, for instance, the guess could actually be a trajectory of target positions.
         """
-        return self.us_guess.shape[0]
+        return self.guess.shape[0]
 
 
 @struct.dataclass
@@ -90,18 +132,46 @@ def optimize(self, to_params: ShootingParams) -> Tuple[jax.Array, jax.Array]:
         raise NotImplementedError
 
 
-# predictive sampling API
+# ########################### #
+# PREDICTIVE SAMPLING METHODS #
+# ########################### #
+
+# generic API
 
 
 @struct.dataclass
-class VanillaPredictiveSamplerParams(ShootingParams):
+class PredictiveSamplerParams(ShootingParams):
     """Parameters for generic predictive sampling methods."""
 
     key: jax.Array  # random key for sampling
 
 
 @struct.dataclass
-class VanillaPredictiveSampler(ShootingAlgorithm):
+class PredictiveSampler(ShootingAlgorithm):
+    """A generic predictive sampler object."""
+
+    def optimize(self, to_params: PredictiveSamplerParams) -> Tuple[jax.Array, jax.Array]:
+        """Optimizes a trajectory using a vanilla predictive sampler.
+
+        Args:
+            to_params: The parameters of the trajectory optimizer.
+
+        Returns:
+            xs (shape=(N + 1, nq + nv) or (?)): The optimized trajectory.
+            us (shape=(N, nu) or (?)): The optimized controls.
+        """
+
+
+# vanilla
+
+
+@struct.dataclass
+class VanillaPredictiveSamplerParams(PredictiveSamplerParams):
+    """Parameters for the vanilla predictive sampling method."""
+
+
+@struct.dataclass
+class VanillaPredictiveSampler(PredictiveSampler):
     """A vanilla predictive sampler object.
 
     The following choices are made:
@@ -123,8 +193,8 @@ def optimize(self, to_params: VanillaPredictiveSamplerParams) -> Tuple[jax.Array
             to_params: The parameters of the trajectory optimizer.
 
         Returns:
-            xs (shape=(N + 1, nq + nv)): The optimized trajectory.
-            us (shape=(N, nu)): The optimized controls.
+            xs_star (shape=(N + 1, nq + nv)): The optimized trajectory.
+            us_star (shape=(N, nu)): The optimized controls.
         """
         # unpack the params
         m = self.model
@@ -132,7 +202,7 @@ def optimize(self, to_params: VanillaPredictiveSamplerParams) -> Tuple[jax.Array
         stdev = self.stdev
 
         x0 = to_params.x0
-        us_guess = to_params.us_guess
+        us_guess = to_params.guess
         N = to_params.N
         key = to_params.key
 
@@ -155,3 +225,76 @@ def optimize(self, to_params: VanillaPredictiveSamplerParams) -> Tuple[jax.Array
         xs_star = lax.dynamic_slice(xs_samples, (best_idx, 0, 0), (1, N + 1, m.nq + m.nv))[0]  # (N + 1, nq + nv)
         us_star = lax.dynamic_slice(us_samples, (best_idx, 0, 0), (1, N, m.nu))[0]  # (N, nu)
         return xs_star, us_star
+
+
+# PD predictive sampling
+
+
+@struct.dataclass
+class PDPredictiveSamplerParams(PredictiveSamplerParams):
+    """Parameters for the PD controller predictive sampler."""
+
+    kp: float  # proportional gain
+    kd: float  # derivative gain
+
+
+@struct.dataclass
+class PDPredictiveSampler(PredictiveSampler):
+    """A PD predictive sampler object.
+
+    The following choices are made:
+    (1) the model parameters are fixed, and are therefore a field of this dataclass;
+    (2) the control sequence is parameterized as a ZOH sequence instead of a spline;
+    (3) the guess supplied is actually a trajectory of positions and the control signal is computed via PD control;
+    (4) the cost function is quadratic in the states and controls.
+    """
+
+    model: mjx.Model
+    cost_function: CostFunction
+    nsamples: int = struct.field(pytree_node=False)
+    stdev: float = struct.field(pytree_node=False)  # noise scale, parameters theta_new ~ N(theta, (stdev ** 2) * I)
+
+    def optimize(self, to_params: VanillaPredictiveSamplerParams) -> Tuple[jax.Array, jax.Array]:
+        """Optimizes a trajectory using a vanilla predictive sampler.
+
+        Args:
+            to_params: The parameters of the trajectory optimizer.
+
+        Returns:
+            xs_star (shape=(N + 1, nq + nv)): The optimized trajectory.
+            us_star (shape=(N, nu)): The optimized controls.
+        """
+        # unpack the params
+        m = self.model
+        nsamples = self.nsamples
+        stdev = self.stdev
+
+        x0 = to_params.x0
+        qs_guess = to_params.guess
+        N = to_params.N
+        key = to_params.key
+
+        kp = to_params.kp
+        kd = to_params.kd
+
+        # sample random position trajectories
+        noise = jnp.concatenate(
+            (jnp.zeros((1, N, m.nq)), jax.random.normal(key, shape=(nsamples - 1, N, m.nq)) * stdev), axis=0
+        )
+        _qgs_samples = qs_guess + noise
+
+        # clamping the samples to their position limits
+        limits = m.jnt_range
+        clip_fn = partial(jnp.clip, a_min=limits[:, 0], a_max=limits[:, 1])  # clipping function with limits already set
+        qgs_samples = vmap(vmap(clip_fn))(_qgs_samples)  # apply limits only to the last dim, need a nested vmap
+        vgs_samples = jnp.zeros_like(qgs_samples)
+        xgs_samples = jnp.concatenate((qgs_samples, vgs_samples), axis=-1)  # (nsamples, N, nq + nv)
+
+        # predict many samples, evaluate them, and return the best trajectory tuple
+        # vmap over the input data and the control trajectories
+        xs_samples, us_samples = vmap(shoot_pd, in_axes=(None, None, 0, None, None))(m, x0, xgs_samples, kp, kd)
+        costs, _ = vmap(self.cost_function.cost, in_axes=(0, 0, None))(xs_samples, us_samples, None)  # (nsamples,)
+        best_idx = jnp.argmin(costs)
+        xs_star = lax.dynamic_slice(xs_samples, (best_idx, 0, 0), (1, N + 1, m.nq + m.nv))[0]  # (N + 1, nq + nv)
+        us_star = lax.dynamic_slice(us_samples, (best_idx, 0, 0), (1, N, m.nu))[0]  # (N, nu)
+        return xs_star, us_star
diff --git a/examples/control/predictive_sampling.py b/examples/control/predictive_sampling.py
index 82fe4a0e..519290ce 100644
--- a/examples/control/predictive_sampling.py
+++ b/examples/control/predictive_sampling.py
@@ -7,7 +7,10 @@
 import numpy as np
 from jax import jit
 
-from ambersim.control.predictive_control import PredictiveSamplingController, PredictiveSamplingControllerParams
+from ambersim.control.predictive_control import (
+    VanillaPredictiveSamplingController,
+    VanillaPredictiveSamplingControllerParams,
+)
 from ambersim.trajopt.cost import StaticGoalQuadraticCost
 from ambersim.trajopt.shooting import VanillaPredictiveSampler
 from ambersim.utils.io_utils import load_mj_model_from_file, mj_to_mjx_model_and_data
@@ -56,10 +59,10 @@
     xg=jnp.concatenate((q_goal, jnp.zeros(16))),
 )
 ps = VanillaPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
-controller = PredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
+controller = VanillaPredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
 jit_compute = jit(
     lambda key, x_meas, us_guess: controller.compute_with_us_star(
-        PredictiveSamplingControllerParams(key=key, x=x_meas, us_guess=us_guess)
+        VanillaPredictiveSamplingControllerParams(key=key, x=x_meas, guess=us_guess)
     )
 )
 print("Controller created! Simulating...")
diff --git a/tests/trajopt/test_predictive_sampler.py b/tests/trajopt/test_predictive_sampler.py
index 7111fc05..f66f4b9a 100644
--- a/tests/trajopt/test_predictive_sampler.py
+++ b/tests/trajopt/test_predictive_sampler.py
@@ -8,17 +8,28 @@
 
 from ambersim.trajopt.base import CostFunctionParams
 from ambersim.trajopt.cost import StaticGoalQuadraticCost
-from ambersim.trajopt.shooting import VanillaPredictiveSampler, VanillaPredictiveSamplerParams, shoot
+from ambersim.trajopt.shooting import (
+    PDPredictiveSampler,
+    PDPredictiveSamplerParams,
+    VanillaPredictiveSampler,
+    VanillaPredictiveSamplerParams,
+    shoot,
+    shoot_pd,
+)
 from ambersim.utils.io_utils import load_mjx_model_and_data_from_file
 
 os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"  # fixes OOM error
 
+# ########################## #
+# VANILLA PREDICTIVE SAMPLER #
+# ########################## #
+
 
 @pytest.fixture
 def vps_data():
     """Makes data required for testing vanilla predictive sampling."""
     # initializing the predictive sampler
-    model, _ = load_mjx_model_and_data_from_file("models/barrett_hand/bh280.xml", force_float=False)
+    model, _ = load_mjx_model_and_data_from_file("models/allegro_hand/right_hand_motor.xml", force_float=False)
     model = model.replace(
         opt=model.opt.replace(
             timestep=0.002,  # dt
@@ -26,7 +37,6 @@ def vps_data():
             ls_iterations=4,  # number of line search iterations along step direction
             integrator=0,  # Euler semi-implicit integration
             solver=2,  # Newton solver
-            disableflags=DisableBit.CONTACT,  # disable contact for this test
         )
     )
     cost_function = StaticGoalQuadraticCost(
@@ -50,7 +60,7 @@ def test_smoke_VPS(vps_data):
     x0 = jnp.zeros(model.nq + model.nv)
     num_steps = 10
     us_guess = jnp.zeros((num_steps, model.nu))
-    params = VanillaPredictiveSamplerParams(key=key, x0=x0, us_guess=us_guess)
+    params = VanillaPredictiveSamplerParams(key=key, x0=x0, guess=us_guess)
 
     # sampling the best sequence of qs, vs, and us
     optimize_fn = jit(ps.optimize)
@@ -72,7 +82,7 @@ def test_VPS_cost_decrease(vps_data):
     us_guess = jax.random.normal(key=subkey, shape=(batch_size, num_steps, model.nu))
 
     keys = jax.random.split(key, num=batch_size)
-    params = VanillaPredictiveSamplerParams(key=keys, x0=x0, us_guess=us_guess)
+    params = VanillaPredictiveSamplerParams(key=keys, x0=x0, guess=us_guess)
 
     # sampling with the vanilla predictive sampler
     xs_stars, us_stars = vmap(ps.optimize)(params)
@@ -85,3 +95,50 @@ def test_VPS_cost_decrease(vps_data):
     xs_guess = vmap(shoot, in_axes=(None, 0, 0))(model, x0, us_guess)
     costs_guess = vmap_cost(xs_guess, us_guess)
     assert jnp.all(costs_star <= costs_guess)
+
+
+# ##################### #
+# PD PREDICTIVE SAMPLER #
+# ##################### #
+
+
+@pytest.fixture
+def pdps_data():
+    """Makes data required for testing PD predictive sampling."""
+    # initializing the predictive sampler
+    model, _ = load_mjx_model_and_data_from_file("models/allegro_hand/right_hand_position.xml", force_float=False)
+    model = model.replace(
+        opt=model.opt.replace(
+            timestep=0.002,  # dt
+            iterations=1,  # number of Newton steps to take during solve
+            ls_iterations=4,  # number of line search iterations along step direction
+            integrator=0,  # Euler semi-implicit integration
+            solver=2,  # Newton solver
+        )
+    )
+    cost_function = StaticGoalQuadraticCost(
+        Q=jnp.eye(model.nq + model.nv),
+        Qf=10.0 * jnp.eye(model.nq + model.nv),
+        R=0.01 * jnp.eye(model.nu),
+        xg=jnp.zeros(model.nq + model.nv),
+    )
+    nsamples = 100
+    stdev = 0.01
+    ps = PDPredictiveSampler(model=model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
+    return ps, model, cost_function
+
+
+def test_smoke_PDPS(pdps_data):
+    """Simple smoke test to make sure we can run inputs through the PD predictive sampler + jit."""
+    ps, model, _ = pdps_data
+
+    # sampler parameters
+    key = jax.random.PRNGKey(0)  # random seed for the predictive sampler
+    x0 = jnp.zeros(model.nq + model.nv)
+    num_steps = 10
+    qs_guess = jnp.tile(x0[: model.nq], (num_steps, 1))
+    params = PDPredictiveSamplerParams(key=key, x0=x0, guess=qs_guess, kp=1.0, kd=0.1)
+
+    # sampling the best sequence of qs, vs, and us
+    optimize_fn = jit(ps.optimize)
+    assert optimize_fn(params)

From 269c1c91d69a2dfe5cde1391e35e5657522c3fe3 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sat, 9 Dec 2023 22:58:59 -0800
Subject: [PATCH 12/15] added working example of PD predictive sampling

---
 ambersim/control/predictive_control.py        |   9 +-
 ambersim/trajopt/shooting.py                  |  16 ++-
 examples/control/pd_predictive_sampling.py    | 102 ++++++++++++++++++
 ...ling.py => vanilla_predictive_sampling.py} |   2 +-
 4 files changed, 114 insertions(+), 15 deletions(-)
 create mode 100644 examples/control/pd_predictive_sampling.py
 rename examples/control/{predictive_sampling.py => vanilla_predictive_sampling.py} (99%)

diff --git a/ambersim/control/predictive_control.py b/ambersim/control/predictive_control.py
index e75eb1ff..4946d6d9 100644
--- a/ambersim/control/predictive_control.py
+++ b/ambersim/control/predictive_control.py
@@ -121,9 +121,9 @@ def compute(self, ctrl_params: PDPredictiveSamplingControllerParams) -> jax.Arra
         Returns:
             u (shape=(nu,)): The control input.
         """
-        return self.compute_with_us_star(ctrl_params)[0]
+        return self.compute_with_qs_star(ctrl_params)[0]
 
-    def compute_with_us_star(self, ctrl_params: PDPredictiveSamplingControllerParams) -> Tuple[jax.Array, jax.Array]:
+    def compute_with_qs_star(self, ctrl_params: PDPredictiveSamplingControllerParams) -> Tuple[jax.Array, jax.Array]:
         """Computes a control input using forward prediction + the optimal sequence of guesses.
 
         This is needed in practice because the current optimal sequence is used to warm start the sampling distribution
@@ -144,5 +144,6 @@ def compute_with_us_star(self, ctrl_params: PDPredictiveSamplingControllerParams
             kd=ctrl_params.kd,
         )
         xs_star, us_star = self.trajectory_optimizer.optimize(to_params)
-        u = us_star[0, :]
-        return u, us_star
+        # u = us_star[0, :]
+        qs_star = xs_star[:, : self.model.nq]  # the 0th index is the current state, so return the 1st index
+        return qs_star[1, :], qs_star
diff --git a/ambersim/trajopt/shooting.py b/ambersim/trajopt/shooting.py
index 06134b10..7fe492e4 100644
--- a/ambersim/trajopt/shooting.py
+++ b/ambersim/trajopt/shooting.py
@@ -48,13 +48,13 @@ def scan_fn(d, u):
     return xs
 
 
-def shoot_pd(m: mjx.Model, x0: jax.Array, xgs: jax.Array, kp: float, kd: float) -> jax.Array:
+def shoot_pd(m: mjx.Model, x0: jax.Array, qgs: jax.Array, kp: float, kd: float) -> jax.Array:
     """Utility function that shoots a model forward given a sequence of goal states + PD gains.
 
     Args:
         m: The model.
         x0 (shape=(nx,)): The initial state.
-        xgs (shape=(N, nx)): The goal trajectory.
+        qgs (shape=(N, nq)): The goal trajectory.
         kp: The proportional gain.
         kd: The derivative gain.
 
@@ -67,12 +67,10 @@ def shoot_pd(m: mjx.Model, x0: jax.Array, xgs: jax.Array, kp: float, kd: float)
     d = d.replace(qpos=x0[: m.nq], qvel=x0[m.nq :])  # setting the initial state.
     d = mjx.forward(m, d)  # setting other internal states like acceleration without integrating
 
-    def scan_fn(d, xg):
+    def scan_fn(d, qg):
         """Integrates the model forward one step given the control input u."""
         # applying PD controller
-        qg = xg[: m.nq]
-        vg = xg[m.nq :]
-        u = -kp * (d.qpos - qg) - kd * (d.qvel - vg)
+        u = -kp * (d.qpos - qg) - kd * d.qvel
 
         d = d.replace(ctrl=u)
         d = step(m, d)
@@ -81,7 +79,7 @@ def scan_fn(d, xg):
         return d, xu
 
     # scan over the control inputs to get the trajectory.
-    _, _xus = lax.scan(scan_fn, d, xgs, length=xgs.shape[0])
+    _, _xus = lax.scan(scan_fn, d, qgs, length=qgs.shape[0])
     _xs = _xus[:, : m.nq + m.nv]
     xs = jnp.concatenate((x0[None, :], _xs), axis=0)  # (N + 1, nq + nv)
     us = _xus[:, m.nq + m.nv :]
@@ -287,12 +285,10 @@ def optimize(self, to_params: VanillaPredictiveSamplerParams) -> Tuple[jax.Array
         limits = m.jnt_range
         clip_fn = partial(jnp.clip, a_min=limits[:, 0], a_max=limits[:, 1])  # clipping function with limits already set
         qgs_samples = vmap(vmap(clip_fn))(_qgs_samples)  # apply limits only to the last dim, need a nested vmap
-        vgs_samples = jnp.zeros_like(qgs_samples)
-        xgs_samples = jnp.concatenate((qgs_samples, vgs_samples), axis=-1)  # (nsamples, N, nq + nv)
 
         # predict many samples, evaluate them, and return the best trajectory tuple
         # vmap over the input data and the control trajectories
-        xs_samples, us_samples = vmap(shoot_pd, in_axes=(None, None, 0, None, None))(m, x0, xgs_samples, kp, kd)
+        xs_samples, us_samples = vmap(shoot_pd, in_axes=(None, None, 0, None, None))(m, x0, qgs_samples, kp, kd)
         costs, _ = vmap(self.cost_function.cost, in_axes=(0, 0, None))(xs_samples, us_samples, None)  # (nsamples,)
         best_idx = jnp.argmin(costs)
         xs_star = lax.dynamic_slice(xs_samples, (best_idx, 0, 0), (1, N + 1, m.nq + m.nv))[0]  # (N + 1, nq + nv)
diff --git a/examples/control/pd_predictive_sampling.py b/examples/control/pd_predictive_sampling.py
new file mode 100644
index 00000000..323cd830
--- /dev/null
+++ b/examples/control/pd_predictive_sampling.py
@@ -0,0 +1,102 @@
+import time
+
+import jax
+import jax.numpy as jnp
+import mujoco
+import mujoco.viewer
+import numpy as np
+from jax import jit
+
+from ambersim.control.predictive_control import PDPredictiveSamplingController, PDPredictiveSamplingControllerParams
+from ambersim.trajopt.cost import StaticGoalQuadraticCost
+from ambersim.trajopt.shooting import PDPredictiveSampler
+from ambersim.utils.io_utils import load_mj_model_from_file, mj_to_mjx_model_and_data
+
+# ########## #
+# PARAMETERS #
+# ########## #
+
+dt = 0.001  # rate of reality
+num_phys_steps_per_control_step = 15  # dt * npspcs = rate of control
+
+w_pos = 10.0
+w_vel = 0.01
+w_ctrl = 0.001
+q_goal = jnp.array([0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5, 0.5, 1.0, 0.8, 0.5, 0.5])
+
+nsamples = 100  # number of samples to draw for predictive sampling
+stdev = 0.1  # standard deviation of control parameters to draw
+N = 10  # number of time steps to predict
+
+key = jax.random.PRNGKey(1234)
+
+# ########## #
+# SIMULATION #
+# ########## #
+
+# loading model
+mj_model = load_mj_model_from_file("models/allegro_hand/right_hand_position.xml")
+mj_model.opt.timestep = dt
+
+# defining the predictive controller
+ctrl_model, _ = mj_to_mjx_model_and_data(mj_model)
+ctrl_model = ctrl_model.replace(
+    opt=ctrl_model.opt.replace(
+        timestep=num_phys_steps_per_control_step * dt,  # dt for each step in the controller's internal model
+        iterations=1,  # number of Newton steps to take during solve
+        ls_iterations=1,  # number of line search iterations along step direction
+        integrator=0,  # Euler semi-implicit integration
+        solver=2,  # Newton solver
+    )
+)
+cost_function = StaticGoalQuadraticCost(
+    Q=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
+    Qf=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
+    R=w_ctrl * jnp.eye(ctrl_model.nu),
+    xg=jnp.concatenate((q_goal, jnp.zeros(16))),
+)
+ps = PDPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
+
+kp = 0.5
+kd = 1.0
+controller = PDPredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
+jit_compute = jit(
+    lambda key, x_meas, qgs_guess: controller.compute_with_qs_star(
+        PDPredictiveSamplingControllerParams(key=key, x=x_meas, guess=qgs_guess, kp=kp, kd=kd)
+    )
+)
+print("Controller created! Simulating...")
+
+# simulating forward
+x0 = 2.0 * (jax.random.uniform(key, shape=(ctrl_model.nq + ctrl_model.nv,)) - 0.5)  # random initial state
+
+mj_data = mujoco.MjData(mj_model)
+mj_data.qpos[:] = x0[: mj_model.nq]
+mj_data.qvel[:] = x0[mj_model.nq :]
+
+qgs_guess = jnp.tile(x0[: mj_model.nq], (N, 1))
+
+print("Compiling jit_compute...")
+jit_compute(key, x0, qgs_guess)
+print("Compiled! Beginning simulation...")
+
+with mujoco.viewer.launch_passive(mj_model, mj_data) as viewer:
+    while viewer.is_running():
+        while True:
+            start = time.time()
+            x_meas = jnp.concatenate((mj_data.qpos, mj_data.qvel))
+            qg, _qgs_guess = jit_compute(key, x_meas, qgs_guess)
+            qg.block_until_ready()
+            qgs_guess = _qgs_guess[1:, :]
+            print(f"Controller delay: {time.time() - start}")
+            for _ in range(num_phys_steps_per_control_step):
+                start = time.time()
+                u = -kp * (mj_data.qpos - qg) - kd * mj_data.qvel
+                u = np.clip(u, mj_model.jnt_range[:, 0], mj_model.jnt_range[:, 1])
+                mj_data.ctrl[:] = u
+                mujoco.mj_step(mj_model, mj_data)
+                viewer.sync()
+                elapsed = time.time() - start
+                if elapsed < mj_model.opt.timestep:
+                    time.sleep(mj_model.opt.timestep - elapsed)
+            key = jax.random.split(key)[0]
diff --git a/examples/control/predictive_sampling.py b/examples/control/vanilla_predictive_sampling.py
similarity index 99%
rename from examples/control/predictive_sampling.py
rename to examples/control/vanilla_predictive_sampling.py
index 519290ce..38fd9172 100644
--- a/examples/control/predictive_sampling.py
+++ b/examples/control/vanilla_predictive_sampling.py
@@ -38,7 +38,7 @@
 # ########## #
 
 # loading model
-mj_model = load_mj_model_from_file("models/allegro_hand/right_hand.xml")
+mj_model = load_mj_model_from_file("models/allegro_hand/right_hand_motor.xml")
 mj_model.opt.timestep = dt
 
 # defining the predictive controller

From 55c98399acc05e63cfff64842704ed6d52787caa Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sun, 10 Dec 2023 15:12:19 -0800
Subject: [PATCH 13/15] minor bugfix for PD predictive sampler

---
 .../allegro_hand/left_hand_position.xml       | 259 -----------------
 .../allegro_hand/right_hand_position.xml      | 260 ------------------
 examples/control/pd_predictive_sampling.py    |  14 +-
 3 files changed, 9 insertions(+), 524 deletions(-)
 delete mode 100644 ambersim/models/allegro_hand/left_hand_position.xml
 delete mode 100644 ambersim/models/allegro_hand/right_hand_position.xml

diff --git a/ambersim/models/allegro_hand/left_hand_position.xml b/ambersim/models/allegro_hand/left_hand_position.xml
deleted file mode 100644
index 0c778d13..00000000
--- a/ambersim/models/allegro_hand/left_hand_position.xml
+++ /dev/null
@@ -1,259 +0,0 @@
-<mujoco model="allegro_left">
-  <compiler angle="radian" meshdir="assets" autolimits="true"/>
-
-  <option cone="pyramidal" impratio="10"/>
-
-  <default>
-    <default class="allegro_left">
-      <joint axis="0 1 0" damping=".1"/>
-      <position kp="1"/>
-      <geom density="800"/>
-
-      <default class="visual">
-        <geom type="mesh" contype="0" conaffinity="0" group="2" material="black"/>
-        <default class="palm_visual">
-          <geom mesh="base_link"/>
-        </default>
-        <default class="base_visual">
-          <geom mesh="link_0.0"/>
-        </default>
-        <default class="proximal_visual">
-          <geom mesh="link_1.0"/>
-        </default>
-        <default class="medial_visual">
-          <geom mesh="link_2.0"/>
-        </default>
-        <default class="distal_visual">
-          <geom mesh="link_3.0"/>
-        </default>
-        <default class="fingertip_visual">
-          <geom pos="0 0 0.0267" material="white" mesh="link_3.0_tip"/>
-        </default>
-        <default class="thumbtip_visual">
-          <geom pos="0 0 0.0423" material="white" mesh="link_15.0_tip"/>
-        </default>
-      </default>
-
-      <default class="collision">
-        <geom group="3" type="box" mass="0"/>
-        <default class="palm_collision">
-          <geom size="0.0204 0.0565 0.0475" pos="-0.0093 0 -0.0475"/>
-        </default>
-        <default class="base_collision">
-          <geom size="0.0098 0.01375 0.0082" pos="0 0 0.0082"/>
-          <default class="thumb_base_collision">
-            <geom size="0.0179 0.017 0.02275" pos="-0.0179 0.009 0.0145"/>
-          </default>
-        </default>
-        <default class="proximal_collision">
-          <geom size="0.0098 0.01375 0.027" pos="0 0 0.027"/>
-          <default class="thumb_proximal_collision">
-            <geom size="0.0098 0.01375 0.00885" pos="0 0 0.00885"/>
-          </default>
-        </default>
-        <default class="medial_collision">
-          <geom size="0.0098 0.01375 0.0192" pos="0 0 0.0192"/>
-          <default class="thumb_medial_collision">
-            <geom size="0.0098 0.01375 0.0257" pos="0 0 0.0257"/>
-          </default>
-        </default>
-        <default class="distal_collision">
-          <geom size="0.0098 0.01375 0.008" pos="0 0 0.008"/>
-          <default class="thumb_distal_collision">
-            <geom size="0.0098 0.01375 0.0157" pos="0 0 0.0157"/>
-          </default>
-        </default>
-        <default class="fingertip_collision">
-          <geom type="capsule" size="0.012 0.01" pos="0 0 0.019"/>
-          <default class="thumbtip_collision">
-            <geom type="capsule" size="0.012 0.008" pos="0 0 0.035"/>
-          </default>
-        </default>
-      </default>
-
-      <default class="base">
-        <joint axis="0 0 1" range="-0.47 0.47"/>
-        <position ctrlrange="-0.47 0.47"/>
-      </default>
-      <default class="proximal">
-        <joint range="-0.196 1.61"/>
-        <position ctrlrange="-0.196 1.61"/>
-      </default>
-      <default class="medial">
-        <joint range="-0.174 1.709"/>
-        <position ctrlrange="-0.174 1.709"/>
-      </default>
-      <default class="distal">
-        <joint range="-0.227 1.618"/>
-        <position ctrlrange="-0.227 1.618"/>
-      </default>
-      <default class="thumb_base">
-        <joint axis="1 0 0" range="0.263 1.396"/>
-        <position ctrlrange="0.263 1.396"/>
-      </default>
-      <default class="thumb_proximal">
-        <joint axis="0 0 -1" range="-0.105 1.163"/>
-        <position ctrlrange="-0.105 1.163"/>
-      </default>
-      <default class="thumb_medial">
-        <joint range="-0.189 1.644"/>
-        <position ctrlrange="-0.189 1.644"/>
-      </default>
-      <default class="thumb_distal">
-        <joint range="-0.162 1.719"/>
-        <position ctrlrange="-0.162 1.719"/>
-      </default>
-    </default>
-  </default>
-
-  <asset>
-    <material name="black" rgba="0.2 0.2 0.2 1"/>
-    <material name="white" rgba="0.9 0.9 0.9 1"/>
-
-    <mesh file="base_link_left.stl"/>
-    <mesh file="link_0.0.stl"/>
-    <mesh file="link_1.0.stl"/>
-    <mesh file="link_2.0.stl"/>
-    <mesh file="link_3.0.stl"/>
-    <mesh file="link_3.0_tip.stl"/>
-    <mesh file="link_12.0_left.stl"/>
-    <mesh file="link_13.0.stl"/>
-    <mesh file="link_14.0.stl"/>
-    <mesh file="link_15.0.stl"/>
-    <mesh file="link_15.0_tip.stl"/>
-  </asset>
-
-  <worldbody>
-    <body name="palm" quat="0 1 0 1" childclass="allegro_left">
-      <geom quat="1 -1 0 0" class="palm_visual" mesh="base_link_left"/>
-      <geom class="palm_collision"/>
-      <!-- Ring finger -->
-      <body name="rf_base" pos="0 0.0435 -0.001542" quat="0.999048 -0.0436194 0 0">
-        <joint name="rfj0" class="base"/>
-        <geom class="base_visual"/>
-        <geom class="base_collision"/>
-        <body name="rf_proximal" pos="0 0 0.0164">
-          <joint name="rfj1" class="proximal"/>
-          <geom class="proximal_visual"/>
-          <geom class="proximal_collision"/>
-          <body name="rf_medial" pos="0 0 0.054">
-            <joint name="rfj2" class="medial"/>
-            <geom class="medial_visual"/>
-            <geom class="medial_collision"/>
-            <body name="rf_distal" pos="0 0 0.0384">
-              <joint name="rfj3" class="distal"/>
-              <geom class="distal_visual"/>
-              <geom class="distal_collision"/>
-              <body name="rf_tip">
-                <geom class="fingertip_visual"/>
-                <geom class="fingertip_collision"/>
-              </body>
-            </body>
-          </body>
-        </body>
-      </body>
-      <!-- Middle finger -->
-      <body name="mf_base" pos="0 0 0.0007">
-        <joint name="mfj0" class="base"/>
-        <geom class="base_visual"/>
-        <geom class="base_collision"/>
-        <body name="mf_proximal" pos="0 0 0.0164">
-          <joint name="mfj1" class="proximal"/>
-          <geom class="proximal_visual"/>
-          <geom class="proximal_collision"/>
-          <body name="mf_medial" pos="0 0 0.054">
-            <joint name="mfj2" class="medial"/>
-            <geom class="medial_visual"/>
-            <geom class="medial_collision"/>
-            <body name="mf_distal" pos="0 0 0.0384">
-              <joint name="mfj3" class="distal"/>
-              <geom class="distal_visual"/>
-              <geom class="distal_collision"/>
-              <body name="mf_tip">
-                <geom class="fingertip_visual"/>
-                <geom class="fingertip_collision"/>
-              </body>
-            </body>
-          </body>
-        </body>
-      </body>
-      <!-- First finger -->
-      <body name="ff_base" pos="0 -0.0435 -0.001542" quat="0.999048 0.0436194 0 0">
-        <joint name="ffj0" class="base"/>
-        <geom class="base_visual"/>
-        <geom class="base_collision"/>
-        <body name="ff_proximal" pos="0 0 0.0164">
-          <joint name="ffj1" class="proximal"/>
-          <geom class="proximal_visual"/>
-          <geom class="proximal_collision"/>
-          <body name="ff_medial" pos="0 0 0.054">
-            <joint name="ffj2" class="medial"/>
-            <geom class="medial_visual"/>
-            <geom class="medial_collision"/>
-            <body name="ff_distal" pos="0 0 0.0384">
-              <joint name="ffj3" class="distal"/>
-              <geom class="distal_visual"/>
-              <geom class="distal_collision"/>
-              <body name="ff_tip">
-                <geom class="fingertip_visual"/>
-                <geom class="fingertip_collision"/>
-              </body>
-            </body>
-          </body>
-        </body>
-      </body>
-      <!-- Thumb -->
-      <body name="th_base" pos="-0.0182 -0.019333 -0.045987" quat="0.477714 0.521334 -0.521334 0.477714">
-        <joint name="thj0" class="thumb_base"/>
-        <geom quat="0 1 0 0" class="visual" mesh="link_12.0_left"/>
-        <geom class="thumb_base_collision"/>
-        <body name="th_proximal" pos="-0.027 -0.005 0.0399">
-          <joint name="thj1" class="thumb_proximal"/>
-          <geom class="visual" mesh="link_13.0"/>
-          <geom class="thumb_proximal_collision"/>
-          <body name="th_medial" pos="0 0 0.0177">
-            <joint name="thj2" class="thumb_medial"/>
-            <geom class="visual" mesh="link_14.0"/>
-            <geom class="thumb_medial_collision"/>
-            <body name="th_distal" pos="0 0 0.0514">
-              <joint name="thj3" class="thumb_distal"/>
-              <geom class="visual" mesh="link_15.0"/>
-              <geom class="thumb_distal_collision"/>
-              <body name="th_tip">
-                <geom class="thumbtip_visual"/>
-                <geom class="thumbtip_collision"/>
-              </body>
-            </body>
-          </body>
-        </body>
-      </body>
-    </body>
-  </worldbody>
-
-  <contact>
-    <exclude body1="palm" body2="ff_base"/>
-    <exclude body1="palm" body2="mf_base"/>
-    <exclude body1="palm" body2="rf_base"/>
-    <exclude body1="palm" body2="th_base"/>
-    <exclude body1="palm" body2="th_proximal"/>
-  </contact>
-
-  <actuator>
-    <position name="ffa0" joint="ffj0" class="base"/>
-    <position name="ffa1" joint="ffj1" class="proximal"/>
-    <position name="ffa2" joint="ffj2" class="medial"/>
-    <position name="ffa3" joint="ffj3" class="distal"/>
-    <position name="mfa0" joint="mfj0" class="base"/>
-    <position name="mfa1" joint="mfj1" class="proximal"/>
-    <position name="mfa2" joint="mfj2" class="medial"/>
-    <position name="mfa3" joint="mfj3" class="distal"/>
-    <position name="rfa0" joint="rfj0" class="base"/>
-    <position name="rfa1" joint="rfj1" class="proximal"/>
-    <position name="rfa2" joint="rfj2" class="medial"/>
-    <position name="rfa3" joint="rfj3" class="distal"/>
-    <position name="tha0" joint="thj0" class="thumb_base"/>
-    <position name="tha1" joint="thj1" class="thumb_proximal"/>
-    <position name="tha2" joint="thj2" class="thumb_medial"/>
-    <position name="tha3" joint="thj3" class="thumb_distal"/>
-  </actuator>
-</mujoco>
diff --git a/ambersim/models/allegro_hand/right_hand_position.xml b/ambersim/models/allegro_hand/right_hand_position.xml
deleted file mode 100644
index 1b6bf085..00000000
--- a/ambersim/models/allegro_hand/right_hand_position.xml
+++ /dev/null
@@ -1,260 +0,0 @@
-<mujoco model="allegro_right">
-  <compiler angle="radian" meshdir="assets" autolimits="true"/>
-
-  <option cone="pyramidal" impratio="10"/>
-
-  <default>
-    <default class="allegro_right">
-      <joint axis="0 1 0" damping=".1"/>
-      <position kp="1"/>
-      <geom density="800"/>
-
-      <default class="visual">
-        <geom type="mesh" contype="0" conaffinity="0" group="2" material="black"/>
-        <default class="palm_visual">
-          <geom mesh="base_link"/>
-        </default>
-        <default class="base_visual">
-          <geom mesh="link_0.0"/>
-        </default>
-        <default class="proximal_visual">
-          <geom mesh="link_1.0"/>
-        </default>
-        <default class="medial_visual">
-          <geom mesh="link_2.0"/>
-        </default>
-        <default class="distal_visual">
-          <geom mesh="link_3.0"/>
-        </default>
-        <default class="fingertip_visual">
-          <geom pos="0 0 0.0267" material="white" mesh="link_3.0_tip"/>
-        </default>
-        <default class="thumbtip_visual">
-          <geom pos="0 0 0.0423" material="white" mesh="link_15.0_tip"/>
-        </default>
-      </default>
-
-      <default class="collision">
-        <geom group="3" type="box" mass="0"/>
-        <default class="palm_collision">
-          <geom size="0.0204 0.0565 0.0475" pos="-0.0093 0 -0.0475"/>
-        </default>
-        <default class="base_collision">
-          <geom size="0.0098 0.01375 0.0082" pos="0 0 0.0082"/>
-          <default class="thumb_base_collision">
-            <geom size="0.0179 0.017 0.02275" pos="-0.0179 0.009 0.0145"/>
-          </default>
-        </default>
-        <default class="proximal_collision">
-          <geom size="0.0098 0.01375 0.027" pos="0 0 0.027"/>
-          <default class="thumb_proximal_collision">
-            <geom size="0.0098 0.01375 0.00885" pos="0 0 0.00885"/>
-          </default>
-        </default>
-        <default class="medial_collision">
-          <geom size="0.0098 0.01375 0.0192" pos="0 0 0.0192"/>
-          <default class="thumb_medial_collision">
-            <geom size="0.0098 0.01375 0.0257" pos="0 0 0.0257"/>
-          </default>
-        </default>
-        <default class="distal_collision">
-          <geom size="0.0098 0.01375 0.008" pos="0 0 0.008"/>
-          <default class="thumb_distal_collision">
-            <geom size="0.0098 0.01375 0.0157" pos="0 0 0.0157"/>
-          </default>
-        </default>
-        <default class="fingertip_collision">
-          <geom type="capsule" size="0.012 0.01" pos="0 0 0.019"/>
-          <default class="thumbtip_collision">
-            <geom type="capsule" size="0.012 0.008" pos="0 0 0.035"/>
-          </default>
-        </default>
-      </default>
-
-      <default class="base">
-        <joint axis="0 0 1" range="-0.47 0.47"/>
-        <position ctrlrange="-0.47 0.47"/>
-      </default>
-      <default class="proximal">
-        <joint range="-0.196 1.61"/>
-        <position ctrlrange="-0.196 1.61"/>
-      </default>
-      <default class="medial">
-        <joint range="-0.174 1.709"/>
-        <position ctrlrange="-0.174 1.709"/>
-      </default>
-      <default class="distal">
-        <joint range="-0.227 1.618"/>
-        <position ctrlrange="-0.227 1.618"/>
-      </default>
-      <default class="thumb_base">
-        <joint axis="-1 0 0" range="0.263 1.396"/>
-        <position ctrlrange="0.263 1.396"/>
-      </default>
-      <default class="thumb_proximal">
-        <joint axis="0 0 1" range="-0.105 1.163"/>
-        <position ctrlrange="-0.105 1.163"/>
-      </default>
-      <default class="thumb_medial">
-        <joint range="-0.189 1.644"/>
-        <position ctrlrange="-0.189 1.644"/>
-      </default>
-      <default class="thumb_distal">
-        <joint range="-0.162 1.719"/>
-        <position ctrlrange="-0.162 1.719"/>
-      </default>
-    </default>
-  </default>
-
-  <asset>
-    <material name="black" rgba="0.2 0.2 0.2 1"/>
-    <material name="white" rgba="0.9 0.9 0.9 1"/>
-
-    <mesh file="base_link.stl"/>
-    <mesh file="link_0.0.stl"/>
-    <mesh file="link_1.0.stl"/>
-    <mesh file="link_2.0.stl"/>
-    <mesh file="link_3.0.stl"/>
-    <mesh file="link_3.0_tip.stl"/>
-    <mesh file="link_12.0_right.stl"/>
-    <mesh file="link_13.0.stl"/>
-    <mesh file="link_14.0.stl"/>
-    <mesh file="link_15.0.stl"/>
-    <mesh file="link_15.0_tip.stl"/>
-  </asset>
-
-  <worldbody>
-    <body name="palm" quat="0 1 0 1" childclass="allegro_right">
-      <!-- <inertial mass="0.4154" pos="0 0 0.0475" diaginertia="1e-4 1e-4 1e-4"/> -->
-      <geom class="palm_visual" mesh="base_link"/>
-      <geom class="palm_collision"/>
-      <!-- First finger -->
-      <body name="ff_base" pos="0 0.0435 -0.001542" quat="0.999048 -0.0436194 0 0">
-        <joint name="ffj0" class="base"/>
-        <geom class="base_visual"/>
-        <geom class="base_collision"/>
-        <body name="ff_proximal" pos="0 0 0.0164">
-          <joint name="ffj1" class="proximal"/>
-          <geom class="proximal_visual"/>
-          <geom class="proximal_collision"/>
-          <body name="ff_medial" pos="0 0 0.054">
-            <joint name="ffj2" class="medial"/>
-            <geom class="medial_visual"/>
-            <geom class="medial_collision"/>
-            <body name="ff_distal" pos="0 0 0.0384">
-              <joint name="ffj3" class="distal"/>
-              <geom class="distal_visual"/>
-              <geom class="distal_collision"/>
-              <body name="ff_tip">
-                <geom class="fingertip_visual"/>
-                <geom class="fingertip_collision"/>
-              </body>
-            </body>
-          </body>
-        </body>
-      </body>
-      <!-- Middle finger -->
-      <body name="mf_base" pos="0 0 0.0007">
-        <joint name="mfj0" class="base"/>
-        <geom class="base_visual"/>
-        <geom class="base_collision"/>
-        <body name="mf_proximal" pos="0 0 0.0164">
-          <joint name="mfj1" class="proximal"/>
-          <geom class="proximal_visual"/>
-          <geom class="proximal_collision"/>
-          <body name="mf_medial" pos="0 0 0.054">
-            <joint name="mfj2" class="medial"/>
-            <geom class="medial_visual"/>
-            <geom class="medial_collision"/>
-            <body name="mf_distal" pos="0 0 0.0384">
-              <joint name="mfj3" class="distal"/>
-              <geom class="distal_visual"/>
-              <geom class="distal_collision"/>
-              <body name="mf_tip">
-                <geom class="fingertip_visual"/>
-                <geom class="fingertip_collision"/>
-              </body>
-            </body>
-          </body>
-        </body>
-      </body>
-      <!-- Ring finger -->
-      <body name="rf_base" pos="0 -0.0435 -0.001542" quat="0.999048 0.0436194 0 0">
-        <joint name="rfj0" class="base"/>
-        <geom class="base_visual"/>
-        <geom class="base_collision"/>
-        <body name="rf_proximal" pos="0 0 0.0164">
-          <joint name="rfj1" class="proximal"/>
-          <geom class="proximal_visual"/>
-          <geom class="proximal_collision"/>
-          <body name="rf_medial" pos="0 0 0.054">
-            <joint name="rfj2" class="medial"/>
-            <geom class="medial_visual"/>
-            <geom class="medial_collision"/>
-            <body name="rf_distal" pos="0 0 0.0384">
-              <joint name="rfj3" class="distal"/>
-              <geom class="distal_visual"/>
-              <geom class="distal_collision"/>
-              <body name="rf_tip">
-                <geom class="fingertip_visual"/>
-                <geom class="fingertip_collision"/>
-              </body>
-            </body>
-          </body>
-        </body>
-      </body>
-      <!-- Thumb -->
-      <body name="th_base" pos="-0.0182 0.019333 -0.045987" quat="0.477714 -0.521334 -0.521334 -0.477714">
-        <joint name="thj0" class="thumb_base"/>
-        <geom class="visual" mesh="link_12.0_right"/>
-        <geom class="thumb_base_collision"/>
-        <body name="th_proximal" pos="-0.027 0.005 0.0399">
-          <joint name="thj1" class="thumb_proximal"/>
-          <geom class="visual" mesh="link_13.0"/>
-          <geom class="thumb_proximal_collision"/>
-          <body name="th_medial" pos="0 0 0.0177">
-            <joint name="thj2" class="thumb_medial"/>
-            <geom class="visual" mesh="link_14.0"/>
-            <geom class="thumb_medial_collision"/>
-            <body name="th_distal" pos="0 0 0.0514">
-              <joint name="thj3" class="thumb_distal"/>
-              <geom class="visual" mesh="link_15.0"/>
-              <geom class="thumb_distal_collision"/>
-              <body name="th_tip">
-                <geom class="thumbtip_visual"/>
-                <geom class="thumbtip_collision"/>
-              </body>
-            </body>
-          </body>
-        </body>
-      </body>
-    </body>
-  </worldbody>
-
-  <contact>
-    <exclude body1="palm" body2="ff_base"/>
-    <exclude body1="palm" body2="mf_base"/>
-    <exclude body1="palm" body2="rf_base"/>
-    <exclude body1="palm" body2="th_base"/>
-    <exclude body1="palm" body2="th_proximal"/>
-  </contact>
-
-  <actuator>
-    <position name="ffa0" joint="ffj0" class="base"/>
-    <position name="ffa1" joint="ffj1" class="proximal"/>
-    <position name="ffa2" joint="ffj2" class="medial"/>
-    <position name="ffa3" joint="ffj3" class="distal"/>
-    <position name="mfa0" joint="mfj0" class="base"/>
-    <position name="mfa1" joint="mfj1" class="proximal"/>
-    <position name="mfa2" joint="mfj2" class="medial"/>
-    <position name="mfa3" joint="mfj3" class="distal"/>
-    <position name="rfa0" joint="rfj0" class="base"/>
-    <position name="rfa1" joint="rfj1" class="proximal"/>
-    <position name="rfa2" joint="rfj2" class="medial"/>
-    <position name="rfa3" joint="rfj3" class="distal"/>
-    <position name="tha0" joint="thj0" class="thumb_base"/>
-    <position name="tha1" joint="thj1" class="thumb_proximal"/>
-    <position name="tha2" joint="thj2" class="thumb_medial"/>
-    <position name="tha3" joint="thj3" class="thumb_distal"/>
-  </actuator>
-</mujoco>
diff --git a/examples/control/pd_predictive_sampling.py b/examples/control/pd_predictive_sampling.py
index 323cd830..bf116d4c 100644
--- a/examples/control/pd_predictive_sampling.py
+++ b/examples/control/pd_predictive_sampling.py
@@ -12,6 +12,10 @@
 from ambersim.trajopt.shooting import PDPredictiveSampler
 from ambersim.utils.io_utils import load_mj_model_from_file, mj_to_mjx_model_and_data
 
+"""
+By far the most sensitive parameters for this controller parameterization are the rollout horizon N and the proportional gain.
+"""
+
 # ########## #
 # PARAMETERS #
 # ########## #
@@ -26,7 +30,7 @@
 
 nsamples = 100  # number of samples to draw for predictive sampling
 stdev = 0.1  # standard deviation of control parameters to draw
-N = 10  # number of time steps to predict
+N = 8  # number of time steps to predict
 
 key = jax.random.PRNGKey(1234)
 
@@ -35,7 +39,7 @@
 # ########## #
 
 # loading model
-mj_model = load_mj_model_from_file("models/allegro_hand/right_hand_position.xml")
+mj_model = load_mj_model_from_file("models/allegro_hand/right_hand_motor.xml")
 mj_model.opt.timestep = dt
 
 # defining the predictive controller
@@ -57,8 +61,8 @@
 )
 ps = PDPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
 
-kp = 0.5
-kd = 1.0
+kp = 5.0
+kd = 0.1
 controller = PDPredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
 jit_compute = jit(
     lambda key, x_meas, qgs_guess: controller.compute_with_qs_star(
@@ -92,7 +96,7 @@
             for _ in range(num_phys_steps_per_control_step):
                 start = time.time()
                 u = -kp * (mj_data.qpos - qg) - kd * mj_data.qvel
-                u = np.clip(u, mj_model.jnt_range[:, 0], mj_model.jnt_range[:, 1])
+                u = np.clip(u, mj_model.actuator_ctrlrange[:, 0], mj_model.actuator_ctrlrange[:, 1])
                 mj_data.ctrl[:] = u
                 mujoco.mj_step(mj_model, mj_data)
                 viewer.sync()

From 9ce793874f100ae6292e63871e3cd44754495883 Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sun, 10 Dec 2023 22:35:23 -0800
Subject: [PATCH 14/15] added cube to models

---
 .../models/allegro_hand/assets/fileback.png   | Bin 0 -> 8601 bytes
 .../models/allegro_hand/assets/filedown.png   | Bin 0 -> 9816 bytes
 .../models/allegro_hand/assets/filefront.png  | Bin 0 -> 10155 bytes
 .../models/allegro_hand/assets/fileleft.png   | Bin 0 -> 7959 bytes
 .../models/allegro_hand/assets/fileright.png  | Bin 0 -> 1406 bytes
 .../models/allegro_hand/assets/fileup.png     | Bin 0 -> 3973 bytes
 .../models/allegro_hand/assets/grayback.png   | Bin 0 -> 8356 bytes
 .../models/allegro_hand/assets/graydown.png   | Bin 0 -> 9630 bytes
 .../models/allegro_hand/assets/grayfront.png  | Bin 0 -> 9535 bytes
 .../models/allegro_hand/assets/grayleft.png   | Bin 0 -> 5126 bytes
 .../models/allegro_hand/assets/grayright.png  | Bin 0 -> 1237 bytes
 .../models/allegro_hand/assets/grayup.png     | Bin 0 -> 2788 bytes
 ambersim/models/allegro_hand/cube.xml         |  29 ++++++++++++++++++
 .../{left_hand_motor.xml => left_hand.xml}    |   0
 .../{right_hand_motor.xml => right_hand.xml}  |   0
 ambersim/models/allegro_hand/scene_left.xml   |   6 +---
 ambersim/models/allegro_hand/scene_right.xml  |   6 +---
 17 files changed, 31 insertions(+), 10 deletions(-)
 create mode 100644 ambersim/models/allegro_hand/assets/fileback.png
 create mode 100644 ambersim/models/allegro_hand/assets/filedown.png
 create mode 100644 ambersim/models/allegro_hand/assets/filefront.png
 create mode 100644 ambersim/models/allegro_hand/assets/fileleft.png
 create mode 100644 ambersim/models/allegro_hand/assets/fileright.png
 create mode 100644 ambersim/models/allegro_hand/assets/fileup.png
 create mode 100644 ambersim/models/allegro_hand/assets/grayback.png
 create mode 100644 ambersim/models/allegro_hand/assets/graydown.png
 create mode 100644 ambersim/models/allegro_hand/assets/grayfront.png
 create mode 100644 ambersim/models/allegro_hand/assets/grayleft.png
 create mode 100644 ambersim/models/allegro_hand/assets/grayright.png
 create mode 100644 ambersim/models/allegro_hand/assets/grayup.png
 create mode 100644 ambersim/models/allegro_hand/cube.xml
 rename ambersim/models/allegro_hand/{left_hand_motor.xml => left_hand.xml} (100%)
 rename ambersim/models/allegro_hand/{right_hand_motor.xml => right_hand.xml} (100%)

diff --git a/ambersim/models/allegro_hand/assets/fileback.png b/ambersim/models/allegro_hand/assets/fileback.png
new file mode 100644
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diff --git a/ambersim/models/allegro_hand/cube.xml b/ambersim/models/allegro_hand/cube.xml
new file mode 100644
index 00000000..c857243d
--- /dev/null
+++ b/ambersim/models/allegro_hand/cube.xml
@@ -0,0 +1,29 @@
+<mujoco>
+  <asset>
+    <texture name="cube" type="cube"
+             fileup="assets/fileup.png" fileback="assets/fileback.png"
+             filedown="assets/filedown.png" filefront="assets/filefront.png"
+             fileleft="assets/fileleft.png" fileright="assets/fileright.png"/>
+    <material name="cube" texture="cube"/>
+    <texture name="graycube" type="cube" fileup="assets/grayup.png"
+             fileback="assets/grayback.png" filedown="assets/graydown.png"
+             filefront="assets/grayfront.png" fileleft="assets/grayleft.png"
+             fileright="assets/grayright.png"/>
+    <material name="graycube" texture="graycube"/>
+  </asset>
+  <worldbody>
+    <light pos="0 0 1"/>
+    <body name="cube" pos="0.0 0.0 0.035" quat="1 0 0 0">
+      <freejoint/>
+      <geom name="cube" type="box" size=".022 .022 .022" mass=".126" material="cube"/>
+    </body>
+  </worldbody>
+
+   <sensor>
+        <framepos name="trace0" objtype="body" objname="cube"/>
+        <framepos name="cube_position" objtype="body" objname="cube"/>
+        <framequat name="cube_orientation" objtype="body" objname="cube"/>
+        <framelinvel name="cube_linear_velocity" objtype="body" objname="cube"/>
+        <frameangvel name="cube_angular_velocity" objtype="body" objname="cube"/>
+    </sensor>
+</mujoco>
diff --git a/ambersim/models/allegro_hand/left_hand_motor.xml b/ambersim/models/allegro_hand/left_hand.xml
similarity index 100%
rename from ambersim/models/allegro_hand/left_hand_motor.xml
rename to ambersim/models/allegro_hand/left_hand.xml
diff --git a/ambersim/models/allegro_hand/right_hand_motor.xml b/ambersim/models/allegro_hand/right_hand.xml
similarity index 100%
rename from ambersim/models/allegro_hand/right_hand_motor.xml
rename to ambersim/models/allegro_hand/right_hand.xml
diff --git a/ambersim/models/allegro_hand/scene_left.xml b/ambersim/models/allegro_hand/scene_left.xml
index bce144ed..c1ad48f9 100644
--- a/ambersim/models/allegro_hand/scene_left.xml
+++ b/ambersim/models/allegro_hand/scene_left.xml
@@ -1,5 +1,6 @@
 <mujoco model="left_allegro_hand scene">
   <include file="left_hand.xml"/>
+  <include file="cube.xml"/>
 
   <statistic center="0 0 0" extent="0.3"/>
 
@@ -20,10 +21,5 @@
     <light pos="0 0 1"/>
     <light pos="0.3 0 1.5" dir="0 0 -1" directional="true"/>
     <geom name="floor" pos="0 0 -0.1" size="0 0 0.05" type="plane" material="groundplane"/>
-    <body name="object" pos="0 0 0.1">
-      <freejoint/>
-      <geom type="ellipsoid" size="0.03 0.04 0.02" rgba="0.5 0.7 0.5 1" condim="6" priority="1"
-        friction="0.7 0.002 0.002"/>
-    </body>
   </worldbody>
 </mujoco>
diff --git a/ambersim/models/allegro_hand/scene_right.xml b/ambersim/models/allegro_hand/scene_right.xml
index 36380ece..b1f79cf6 100644
--- a/ambersim/models/allegro_hand/scene_right.xml
+++ b/ambersim/models/allegro_hand/scene_right.xml
@@ -1,5 +1,6 @@
 <mujoco model="right_allegro_hand scene">
   <include file="right_hand.xml"/>
+  <include file="cube.xml"/>
 
   <statistic center="0 0 0" extent="0.3"/>
 
@@ -20,10 +21,5 @@
     <light pos="0 0 1"/>
     <light pos="0.3 0 1.5" dir="0 0 -1" directional="true"/>
     <geom name="floor" pos="0 0 -0.1" size="0 0 0.05" type="plane" material="groundplane"/>
-    <body name="object" pos="0 0 0.1">
-      <freejoint/>
-      <geom type="ellipsoid" size="0.03 0.04 0.02" rgba="0.5 0.7 0.5 1" condim="6" priority="1"
-        friction="0.7 0.002 0.002"/>
-    </body>
   </worldbody>
 </mujoco>

From 230023fab8cf1b60d296669cdd345de3c58b8a3a Mon Sep 17 00:00:00 2001
From: alberthli <alberthli@caltech.edu>
Date: Sun, 10 Dec 2023 22:37:41 -0800
Subject: [PATCH 15/15] added crappy broken palm up script

---
 examples/control/algr_palmup.py               | 147 ++++++++++++++++++
 examples/control/pd_predictive_sampling.py    |   4 +-
 .../control/vanilla_predictive_sampling.py    |   4 +-
 pyproject.toml                                |   2 +-
 4 files changed, 152 insertions(+), 5 deletions(-)
 create mode 100644 examples/control/algr_palmup.py

diff --git a/examples/control/algr_palmup.py b/examples/control/algr_palmup.py
new file mode 100644
index 00000000..89d6a47c
--- /dev/null
+++ b/examples/control/algr_palmup.py
@@ -0,0 +1,147 @@
+import time
+
+import jax
+import jax.numpy as jnp
+import mujoco
+import mujoco.viewer
+import numpy as np
+from jax import jit
+
+# from ambersim.control.predictive_control import PDPredictiveSamplingController, PDPredictiveSamplingControllerParams
+from ambersim.control.predictive_control import (
+    VanillaPredictiveSamplingController,
+    VanillaPredictiveSamplingControllerParams,
+)
+from ambersim.trajopt.cost import StaticGoalQuadraticCost
+
+# from ambersim.trajopt.shooting import PDPredictiveSampler
+from ambersim.trajopt.shooting import VanillaPredictiveSampler
+from ambersim.utils.io_utils import load_mj_model_from_file, mj_to_mjx_model_and_data
+
+"""Important info:
+* the cube's states come after the hand's states
+* the allegro hand is floating but fixed in space
+
+TODOs:
+* implement a check for the cube being stuck
+* add some reasonable cost terms for the palm up task (see the mjpc code + talk to vince)
+* add support for the PD controller, but we have to more clever about underactuation
+"""
+
+# ##################### #
+# CUBE TARGET POSITIONS #
+# ##################### #
+
+# position
+POS = jnp.array([0.05, 0.0, 0.05])  # move the cube over the fingers
+Z_FAIL = -0.05  # floor at -0.1
+
+# rotations
+IDENTITY = jnp.array([1.0, 0.0, 0.0, 0.0])
+X90 = jnp.array([0.7071068, 0.7071068, 0.0, 0.0])
+X180 = jnp.array([0.0, 1.0, 0.0, 0.0])
+X270 = jnp.array([-0.7071068, 0.7071068, 0.0, 0.0])
+Y90 = jnp.array([0.7071068, 0.0, 0.7071068, 0.0])
+Y180 = jnp.array([0.0, 0.0, 1.0, 0.0])
+Y270 = jnp.array([-0.7071068, 0.0, 0.7071068, 0.0])
+Z90 = jnp.array([0.7071068, 0.0, 0.0, 0.7071068])
+Z180 = jnp.array([0.0, 0.0, 0.0, 1.0])
+Z270 = jnp.array([-0.7071068, 0.0, 0.0, 0.7071068])
+
+# ########## #
+# PARAMETERS #
+# ########## #
+
+dt = 0.001  # rate of reality
+num_phys_steps_per_control_step = 15  # dt * npspcs = rate of control
+
+w_pos = 10.0
+w_vel = 0.001
+w_ctrl = 0.001
+q_goal_algr = jnp.zeros(16)
+q_goal_cube = jnp.concatenate((POS, X90))
+q_goal = jnp.concatenate((q_goal_algr, q_goal_cube))
+
+nsamples = 100  # number of samples to draw for predictive sampling
+stdev = 0.3  # standard deviation of control parameters to draw
+N = 5  # number of time steps to predict
+
+key = jax.random.PRNGKey(1234)
+
+# ########## #
+# SIMULATION #
+# ########## #
+
+# loading model
+mj_model = load_mj_model_from_file("models/allegro_hand/scene_right.xml")
+mj_model.opt.timestep = dt
+
+# defining the predictive controller
+ctrl_model, _ = mj_to_mjx_model_and_data(mj_model)
+ctrl_model = ctrl_model.replace(
+    opt=ctrl_model.opt.replace(
+        timestep=num_phys_steps_per_control_step * dt,  # dt for each step in the controller's internal model
+        iterations=1,  # number of Newton steps to take during solve
+        ls_iterations=1,  # number of line search iterations along step direction
+        integrator=0,  # Euler semi-implicit integration
+        solver=2,  # Newton solver
+    )
+)
+cost_function = StaticGoalQuadraticCost(
+    Q=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
+    Qf=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
+    R=w_ctrl * jnp.eye(ctrl_model.nu),
+    xg=jnp.concatenate((q_goal, jnp.zeros(ctrl_model.nv))),
+)
+ps = VanillaPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
+controller = VanillaPredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
+jit_compute = jit(
+    lambda key, x_meas, us_guess: controller.compute_with_us_star(
+        VanillaPredictiveSamplingControllerParams(key=key, x=x_meas, guess=us_guess)
+    )
+)
+print("Controller created! Simulating...")
+
+# simulating forward
+q0_algr = jnp.zeros(16)
+q0_cube = jnp.concatenate((POS, IDENTITY))
+v0 = jnp.zeros(mj_model.nv)
+x0 = jnp.concatenate((q0_algr, q0_cube, v0))
+
+mj_data = mujoco.MjData(mj_model)
+mj_data.qpos[:] = x0[: mj_model.nq]
+mj_data.qvel[:] = x0[mj_model.nq :]
+
+us_guess = jnp.zeros((N, mj_model.nu))
+
+print("Compiling jit_compute...")
+jit_compute(key, x0, us_guess)
+print("Compiled! Beginning simulation...")
+
+with mujoco.viewer.launch_passive(mj_model, mj_data) as viewer:
+    while viewer.is_running():
+        while True:
+            start = time.time()
+            x_meas = jnp.concatenate((mj_data.qpos, mj_data.qvel))
+            u, _us_guess = jit_compute(key, x_meas, us_guess)
+            us_guess = np.concatenate((_us_guess[1:, :], np.zeros((1, mj_model.nu))))
+            mj_data.ctrl[:] = u
+            print(f"Controller delay: {time.time() - start}")
+            for _ in range(num_phys_steps_per_control_step):
+                start = time.time()
+                mujoco.mj_step(mj_model, mj_data)
+                viewer.sync()
+                elapsed = time.time() - start
+                if elapsed < mj_model.opt.timestep:
+                    time.sleep(mj_model.opt.timestep - elapsed)
+
+            # check whether to reset the cube
+            if mj_data.qpos[-5] <= Z_FAIL:
+                print("*** Cube fell! Resetting... *** ")
+                mj_data.qpos[:] = x0[: mj_model.nq]
+                mj_data.qvel[:] = x0[mj_model.nq :]
+                us_guess = jnp.zeros((N, mj_model.nu))
+                viewer.sync()
+                time.sleep(1.0)
+
+            key = jax.random.split(key)[0]
diff --git a/examples/control/pd_predictive_sampling.py b/examples/control/pd_predictive_sampling.py
index bf116d4c..c4b18b99 100644
--- a/examples/control/pd_predictive_sampling.py
+++ b/examples/control/pd_predictive_sampling.py
@@ -39,7 +39,7 @@
 # ########## #
 
 # loading model
-mj_model = load_mj_model_from_file("models/allegro_hand/right_hand_motor.xml")
+mj_model = load_mj_model_from_file("models/allegro_hand/right_hand.xml")
 mj_model.opt.timestep = dt
 
 # defining the predictive controller
@@ -57,7 +57,7 @@
     Q=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
     Qf=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
     R=w_ctrl * jnp.eye(ctrl_model.nu),
-    xg=jnp.concatenate((q_goal, jnp.zeros(16))),
+    xg=jnp.concatenate((q_goal, jnp.zeros(ctrl_model.nv))),
 )
 ps = PDPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
 
diff --git a/examples/control/vanilla_predictive_sampling.py b/examples/control/vanilla_predictive_sampling.py
index 38fd9172..b1e8dc31 100644
--- a/examples/control/vanilla_predictive_sampling.py
+++ b/examples/control/vanilla_predictive_sampling.py
@@ -38,7 +38,7 @@
 # ########## #
 
 # loading model
-mj_model = load_mj_model_from_file("models/allegro_hand/right_hand_motor.xml")
+mj_model = load_mj_model_from_file("models/allegro_hand/right_hand.xml")
 mj_model.opt.timestep = dt
 
 # defining the predictive controller
@@ -56,7 +56,7 @@
     Q=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
     Qf=jnp.diag(jnp.array([w_pos] * ctrl_model.nq + [w_vel] * ctrl_model.nv)),
     R=w_ctrl * jnp.eye(ctrl_model.nu),
-    xg=jnp.concatenate((q_goal, jnp.zeros(16))),
+    xg=jnp.concatenate((q_goal, jnp.zeros(ctrl_model.nv))),
 )
 ps = VanillaPredictiveSampler(model=ctrl_model, cost_function=cost_function, nsamples=nsamples, stdev=stdev)
 controller = VanillaPredictiveSamplingController(trajectory_optimizer=ps, model=ctrl_model)
diff --git a/pyproject.toml b/pyproject.toml
index 0fb40aa3..79e618d4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,7 +59,7 @@ install-mujoco-from-src = "ambersim._scripts._install_shim:entrypoint"
 include = ["ambersim*"]
 
 [tool.setuptools.package-data]
-"*" = ["*.obj", "*.stl", "*.urdf", "*.xml", "install.sh", "install_mj_source.sh"]
+"*" = ["*.png", "*.obj", "*.stl", "*.urdf", "*.xml", "install.sh", "install_mj_source.sh"]
 
 [tool.black]
 line-length = 120