feat(deploy): add native MotrixSim runtime

AGENTS.md

@@ -22,6 +22,7 @@ MotrixLab 是构建在 MotrixSim 仿真后端之上的强化学习框架，提
 - `motrix_rl_rslrl`：RSLRL PPO provider 插件（Torch）
 - `motrix_deploy`：框架无关的 artifact、运行时契约与部署 CLI
 - `motrix_deploy_mujoco`：SceneCfg 支撑的 MuJoCo 后端插件
+- `motrix_deploy_motrixsim`：SceneCfg 支撑的原生 MotrixSim 部署后端插件
 - `motrix_deploy_unitree`：Unitree SDK2 DDS 硬件后端插件
 - `motrix_deploy_tasks`：具体部署任务实现
 

README.md

@@ -32,7 +32,7 @@ _Microduck locomotion policies trained with MotrixLab, rendered in MotrixRender
 
 ## What is MotrixLab?
 
-**MotrixLab** is an open-source reinforcement learning framework for robot training, built on the high-performance [MotrixSim](https://github.com/Motphys/motrixsim-docs) physics engine. Define an environment once, train it with thousands of parallel environment instances using SKRL, RSL-RL, or the built-in FastSAC, and deploy the resulting policy to MuJoCo or Unitree hardware — all through a single command-line interface.
+**MotrixLab** is an open-source reinforcement learning framework for robot training, built on the high-performance [MotrixSim](https://github.com/Motphys/motrixsim-docs) physics engine. Define an environment once, train it with thousands of parallel environment instances using SKRL, RSL-RL, or the built-in FastSAC, and deploy the resulting policy to MuJoCo, native MotrixSim, or Unitree hardware — all through a single command-line interface.
 
 <div align="center">
   <picture>
@@ -46,7 +46,7 @@ _Microduck locomotion policies trained with MotrixLab, rendered in MotrixRender
 - **Unified Interface**: Provides a concise and unified reinforcement learning training and evaluation interface
 - **Multi-framework Support**: Supports SKRL (JAX/PyTorch), RSLRL (PyTorch), and the built-in FastSAC implementation
 - **Rich Environments**: Includes various robot simulation environments such as basic control, locomotion, and manipulation tasks
-- **Sim-to-Real Deployment**: The same policy code deploys via the deploy CLI — Sim2Sim to MuJoCo, Sim2Real to real hardware
+- **Sim-to-Real Deployment**: The same policy code deploys via the deploy CLI — Sim2Sim to MuJoCo or native MotrixSim, Sim2Real to real hardware
 - **High-precision, High-performance Simulation**: Built on [MotrixSim](https://motrixsim.readthedocs.io/), a high-precision, high-performance physics engine
 - **Visual Training**: Supports real-time rendering and training process visualization
 
@@ -180,6 +180,7 @@ MotrixLab is a [uv](https://docs.astral.sh/uv/) workspace with the following pac
 | --- | --- | --- |
 | **motrix_deploy** | `motrix-deploy` | Artifacts, control sessions, runtime contracts, and deployment CLI |
 | **motrix_deploy_mujoco** | `motrix-deploy-mujoco` | MuJoCo deployment backend plugin |
+| **motrix_deploy_motrixsim** | `motrix-deploy-motrixsim` | Native MotrixSim deployment backend plugin |
 | **motrix_deploy_unitree** | `motrix-deploy-unitree` | Unitree SDK2 DDS hardware backend plugin |
 | **motrix_deploy_tasks** | `motrix-deploy-tasks` | Walking tasks, deployment scenes, and installed Hydra recipes |
 | **motrix_env_core** | `motrix-env-core` | Backend-agnostic environment framework, configuration, registry, and rendering |

README.zh-CN.md

@@ -32,7 +32,7 @@ _使用 MotrixLab 训练的 microduck 行走策略，由 MotrixRender 实时渲
 
 ## MotrixLab 是什么？
 
-**MotrixLab** 是一个开源的机器人强化学习训练框架，构建于高性能的 [MotrixSim](https://github.com/Motphys/motrixsim-docs) 物理引擎之上。环境只需定义一次，即可通过 SKRL、RSLRL 或内置的 FastSAC 使用数千个并行环境实例进行训练，训练得到的策略可部署到 MuJoCo 或 Unitree 硬件 —— 全程只需一个命令行接口。
+**MotrixLab** 是一个开源的机器人强化学习训练框架，构建于高性能的 [MotrixSim](https://github.com/Motphys/motrixsim-docs) 物理引擎之上。环境只需定义一次，即可通过 SKRL、RSLRL 或内置的 FastSAC 使用数千个并行环境实例进行训练，训练得到的策略可部署到 MuJoCo、原生 MotrixSim 或 Unitree 硬件 —— 全程只需一个命令行接口。
 
 <div align="center">
   <picture>
@@ -46,7 +46,7 @@ _使用 MotrixLab 训练的 microduck 行走策略，由 MotrixRender 实时渲
 - **统一接口**: 提供简洁统一的强化学习训练和评估接口
 - **多框架支持**: 支持 SKRL（JAX/PyTorch）、RSLRL（PyTorch）和内置 FastSAC 实现
 - **丰富环境**: 包含基础控制、运动、操作等多种机器人仿真环境
-- **Sim-to-Real 部署**: 同一套策略代码，通过部署 CLI Sim2Sim 到 MuJoCo 仿真、Sim2Real 到真实硬件
+- **Sim-to-Real 部署**: 同一套策略代码，通过部署 CLI Sim2Sim 到 MuJoCo 或原生 MotrixSim 仿真、Sim2Real 到真实硬件
 - **高精度高性能仿真**: 基于 [MotrixSim](https://motrixsim.readthedocs.io/) 高精度、高性能物理仿真引擎
 - **可视化训练**: 支持实时渲染和训练过程可视化
 
@@ -180,6 +180,7 @@ MotrixLab 是一个 [uv](https://docs.astral.sh/uv/) workspace，包含以下 pa
 | --- | --- | --- |
 | **motrix_deploy** | `motrix-deploy` | artifact、控制会话、运行时契约与部署 CLI |
 | **motrix_deploy_mujoco** | `motrix-deploy-mujoco` | MuJoCo 部署 backend plugin |
+| **motrix_deploy_motrixsim** | `motrix-deploy-motrixsim` | 原生 MotrixSim 部署 backend plugin |
 | **motrix_deploy_unitree** | `motrix-deploy-unitree` | Unitree SDK2 DDS 硬件 backend plugin |
 | **motrix_deploy_tasks** | `motrix-deploy-tasks` | 行走任务、部署场景与安装的 Hydra 配方 |
 | **motrix_env_core** | `motrix-env-core` | backend 无关的环境框架、配置、registry 与渲染 |

docs/source/en/user_guide/tutorial/advanced/motrix_deploy.md

@@ -38,7 +38,7 @@ runtime plugin for the target. The recipes work outside the repository without a
 Task defaults select the Hydra runtime group (`sim` or `hardware`); you do not need a separate runtime override.
 Common top-level settings are `artifact`, `duration_s`, and `command`. All target-specific settings live directly under
 `runtime`. `runtime.kind` selects the simulation or hardware path; `runtime.backend` selects the plugin (`mujoco`
-for simulation or `unitree_go2` for hardware). `runtime.backend` is a scalar string selector, not a nested configuration
+or `motrixsim` for simulation, or `unitree_go2` for hardware). `runtime.backend` is a scalar string selector, not a nested configuration
 mapping or Hydra group.
 
 ## 3. Check in MuJoCo
@@ -72,6 +72,21 @@ It creates a MuJoCo runtime with `create_simulation_runtime`, attaches a `Contro
 inference, and robot commands; the runtime handles physics, timing, and the viewer. A raw ONNX file needs explicit task
 preprocessing and action settings, whereas the artifact carries those settings for the CLI.
 
+### Select native MotrixSim
+
+With `motrix-deploy-motrixsim` installed, switch the backend to `motrixsim` to reuse the same artifact and scene recipe:
+
+```bash
+motrix-deploy task=go2-walk-flat/sim artifact=artifacts/go2-walk-flat.deploy \
+  runtime.backend=motrixsim runtime.viewer=true
+```
+
+The Python example also supports native MotrixSim:
+
+```bash
+python examples/deploy_to_sim.py --backend motrixsim
+```
+
 ## 4. Run on the real robot
 
 Suspend the robot, put it in low-level/debug mode, connect Ethernet, and keep an independent emergency stop and operator

docs/source/zh_CN/user_guide/tutorial/advanced/motrix_deploy.md

@@ -34,7 +34,7 @@ artifact 包含 ONNX 策略、机器人与控制设置，以及任务规格。
 部署场景和 Hydra 配方，以及目标对应的运行时插件。配方安装在插件中，在仓库外运行也不需要 workspace 配置路径。
 任务 defaults 选择 Hydra 运行时组（`sim` 或 `hardware`），无需另行覆盖运行时组。
 顶层公共设置为 `artifact`、`duration_s` 和 `command`。所有目标专用设置直接位于 `runtime` 下；
-`runtime.kind` 选择仿真或硬件执行路径；`runtime.backend` 选择插件（仿真为 `mujoco`，硬件为 `unitree_go2`）。
+`runtime.kind` 选择仿真或硬件执行路径；`runtime.backend` 选择插件（仿真为 `mujoco` 或 `motrixsim`，硬件为 `unitree_go2`）。
 `runtime.backend` 是标量字符串选择器，不是嵌套配置映射或 Hydra 配置组。
 
 ## 3. 在 MuJoCo 中检查
@@ -67,6 +67,21 @@ python examples/deploy_to_sim.py --headless --steps 100
 `ControlSession`，并在 `with runtime:` 中运行。控制会话负责输入、观察、推理和机器人指令；运行时负责物理推进、
 调度和 viewer。直接使用 ONNX 文件时需要显式指定任务预处理和动作设置；artifact 则为 CLI 携带这些设置。
 
+### 选择原生 MotrixSim
+
+安装 `motrix-deploy-motrixsim` 后，将 backend 切换为 `motrixsim`，即可复用同一 artifact 和场景配方：
+
+```bash
+motrix-deploy task=go2-walk-flat/sim artifact=artifacts/go2-walk-flat.deploy \
+  runtime.backend=motrixsim runtime.viewer=true
+```
+
+Python 示例同样支持原生 MotrixSim：
+
+```bash
+python examples/deploy_to_sim.py --backend motrixsim
+```
+
 ## 4. 运行真机
 
 将机器人悬空，切换到低层/调试模式，连接网线，并准备好独立急停和操作人员。启动过程可能在遥控器 Start 门控前

examples/deploy_to_sim.py

@@ -35,7 +35,9 @@ class ExampleSceneObjsCfg(SceneObjsCfg):
 
 def main() -> int:
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--backend", choices=("mujoco",), default="mujoco", help="Simulation runtime plugin")
+    parser.add_argument(
+        "--backend", choices=("mujoco", "motrixsim"), default="mujoco", help="Simulation runtime plugin"
+    )
     parser.add_argument("--headless", action="store_true", help="No window; run fixed-step without wall-clock pacing")
     parser.add_argument("--steps", type=int, help="Headless control ticks; GUI mode runs until the window closes")
     args = parser.parse_args()
@@ -65,7 +67,7 @@ def main() -> int:
             haze=(0.1, 0.1, 0.1, 1.0),
             tone_mapping="none",
         ),
-        system_camera=SystemCameraCfg(distance=3.0, elevation=-20.0, azimuth=90.0),
+        system_camera=SystemCameraCfg(lookat=(0.0, 0.0, 0.3), distance=3.0, elevation=-20.0, azimuth=90.0),
     )
     config = SimulationRuntimeConfig(
         scene=scene,

motrix_deploy/src/motrix_deploy/contracts.py

@@ -257,8 +257,6 @@ class RobotCapabilities:
     Attributes:
         control_modes: Canonical joint command modes accepted by the backend.
         state_fields: ``RobotState`` fields populated by every state sample.
-        privileged_state_fields: Subset of state_fields supplied as privileged simulator ground truth,
-            not measurements guaranteed to be available on hardware.
         extra_sensors: Additional entries available in ``RobotState.extras``, mapped to their fixed shapes.
         supports_rendering: Whether the backend can present a live viewer.
         max_command_rate_hz: Highest command update rate accepted by the backend, or ``None`` when unconstrained.
@@ -273,7 +271,6 @@ class RobotCapabilities:
     max_command_rate_hz: float | None = None
     requires_enable: bool = False
     stop_semantics: str = "hold_position"
-    privileged_state_fields: frozenset[str] = frozenset()
 
     def __post_init__(self) -> None:
         _validate_names(self.control_modes, path="capabilities.control_modes")
@@ -288,15 +285,6 @@ def __post_init__(self) -> None:
             not isinstance(name, str) or not name for name in self.state_fields
         ):
             raise ValidationError("capabilities.state_fields", "a frozenset of non-empty names", self.state_fields)
-        if (
-            not isinstance(self.privileged_state_fields, frozenset)
-            or not self.privileged_state_fields <= self.state_fields
-        ):
-            raise ValidationError(
-                "capabilities.privileged_state_fields",
-                "a frozenset subset of state_fields",
-                self.privileged_state_fields,
-            )
         if self.max_command_rate_hz is not None and self.max_command_rate_hz <= 0:
             raise ValidationError("capabilities.max_command_rate_hz", "a positive value", self.max_command_rate_hz)
         if not self.stop_semantics:

motrix_deploy/tests/test_contracts.py

@@ -131,31 +131,6 @@ def test_robot_spec_rejects_invalid_arrays(field: str, value: np.ndarray, error_
         robot_spec(**{field: value})
 
 
-def test_privileged_capabilities_default_empty_and_require_available_fields() -> None:
-    capabilities = RobotCapabilities(control_modes=("joint_servo",), state_fields=frozenset({"base_position"}))
-    assert capabilities.privileged_state_fields == frozenset()
-    privileged = RobotCapabilities(
-        control_modes=("joint_servo",),
-        state_fields=frozenset({"base_position"}),
-        privileged_state_fields=frozenset({"base_position"}),
-    )
-    assert privileged.privileged_state_fields <= privileged.state_fields
-    with pytest.raises(ValidationError, match="capabilities.privileged_state_fields"):
-        RobotCapabilities(
-            control_modes=("joint_servo",),
-            state_fields=frozenset(),
-            privileged_state_fields=frozenset({"base_position"}),
-        )
-
-
-@pytest.mark.parametrize("value", [{"base_position"}, frozenset({"unavailable"})])
-def test_invalid_privileged_state_fields_are_rejected(value: object) -> None:
-    with pytest.raises(ValidationError, match="capabilities.privileged_state_fields"):
-        RobotCapabilities(
-            control_modes=("joint_servo",), state_fields=frozenset({"base_position"}), privileged_state_fields=value
-        )
-
-
 def test_tensor_spec_requires_float32_fixed_shape() -> None:
     with pytest.raises(ValidationError, match="tensor.dtype"):
         TensorSpec(name="input", shape=(1, 4), dtype="float64")

motrix_deploy/tests/test_runtime.py

@@ -696,26 +696,6 @@ def fail(state: RobotState) -> str | None:
     assert backend.events[-2:] == ["stop", "close"]
 
 
-@pytest.mark.parametrize("privileged", [False, True])
-def test_backend_state_metadata_does_not_gate_task_execution(
-    manifest_factory: Callable[[], DeploymentManifest], privileged: bool
-) -> None:
-    manifest = manifest_factory()
-    backend = EnablingBackend(manifest.robot)
-    backend._capabilities = RobotCapabilities(
-        control_modes=("joint_servo",),
-        state_fields=frozenset({"joint_position", "joint_velocity", "base_linear_velocity"}),
-        privileged_state_fields=frozenset({"base_linear_velocity"}) if privileged else frozenset(),
-        requires_enable=True,
-    )
-    runtime, control = _loop(manifest, backend)
-    result = runtime.run(control, steps=1)
-    assert result.success
-    assert len(backend.commands) == 1
-    assert backend.events.index("enable") < backend.events.index("write")
-    assert backend.events[-2:] == ["stop", "close"]
-
-
 def test_required_enable_completes_before_first_policy_write(
     manifest_factory: Callable[[], DeploymentManifest],
 ) -> None: