diff --git a/_bibliography/papers.bib b/_bibliography/papers.bib
index 1631b2e8..0e61d694 100644
--- a/_bibliography/papers.bib
+++ b/_bibliography/papers.bib
@@ -237,6 +237,20 @@ @article{meng2026look
   title    = {Look as You Leap: Planning Simultaneous Motion and Perception for High-{DoF} Robots},
   year     = 2026
 }
+@misc{bukhari2026rmfp,
+  abbr          = {ARXIV},
+  abstract      = {Visuomotor policies learn a direct map from raw sensory observations to robot action sequences. Policies based on Diffusion and Flow Matching capture the multimodal distribution over action sequences in an end-to-end manner. This expressivity comes at the cost of multi-step numerical integration of the learned vector field for action generation, which can be expensive and time-consuming, impeding fast control rates required in robotics applications. Furthermore, robot action sequences are usually defined on a smooth, differentiable manifold, requiring that the learned policy respects the intrinsic geometry of the robot's action space. Here, we present Riemannian MeanFlow Policy (RMFP), which learns the conditioned flow map of the probability path on the robot action manifold. Our formulation employs a flow map consistency objective grounded in the data by a Riemannian Conditional Flow Matching anchor. The flow map consistency condition is stable to train and constrains the learned model to finite-time transport, which yields on-manifold action sequence generation with as few as one network function evaluation. We present results on the spherical LASA and Push-T benchmarks, on the Tool Hang and Transport tasks of the Robomimic suite, and on the Franka Kitchen task with manifold-constrained action generation, and demonstrate that RMFP attains performance competitive with prior work at a lower sampling cost. We also employ RMFP on a real-world robotic manipulation task to demonstrate fast action generation under imperfect sensor measurements in the physical world.},
+  archiveprefix = {arXiv},
+  author        = {S. Talha Bukhari and Austin Garrett and Yi Wei and Ruiqi Ni and Zachary Kingston and Aniket Bera},
+  eprint        = {2609.30127},
+  note          = {Under Review},
+  pdf           = {https://arxiv.org/abs/2609.30127},
+  preview       = {rmfp_pusht.webm},
+  primaryclass  = {cs.RO},
+  projects      = {implicit,realtime},
+  title         = {Faster Visuomotor Policy Learning on Action Manifolds via {R}iemannian {MeanFlow}},
+  year          = 2026
+}
 @misc{chen2026fruitninja,
   abbr          = {ARXIV},
   abstract      = {Projectile interception is a challenging dynamic manipulation problem. Intercepting a thrown object with a robot arm requires reaching a point on the object's path as the object passes through it. Slicing also fixes the blade's velocity and orientation at contact. The goal is therefore a subset of the states of the robot and arrival times that moves as the object falls, and the arm must reach it within its actuator limits in milliseconds. We present FRUITNINJA, an anytime sampling-based planner that grows a tree on the GPU in batches toward the interception manifold. Each edge is an exact cubic whose travel time is found by a parallel search against the arm's dynamics, so every edge satisfies the actuator limits. Plans are ranked by a risk-aware objective over the uncertainty in the object's position and the arm's arrival time. We evaluate on a Franka Research 3 against six baselines in a calibrated real-time simulator, where FRUITNINJA cuts 96.7\% of tosses in the open and 68.3\% among five obstacles, versus the best baseline's 68.3\% and 35.0\% respectively.},
diff --git a/assets/img/publication_preview/rmfp_pusht.webm b/assets/img/publication_preview/rmfp_pusht.webm
new file mode 100644
index 00000000..92c54d9c
Binary files /dev/null and b/assets/img/publication_preview/rmfp_pusht.webm differ
diff --git a/assets/img/publication_preview/rmfp_pusht.webp b/assets/img/publication_preview/rmfp_pusht.webp
new file mode 100644
index 00000000..a0e78106
Binary files /dev/null and b/assets/img/publication_preview/rmfp_pusht.webp differ