EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space
Overview
EmbodiSteer is a training-free framework that steers embodiment-agnostic visuomotor policies toward zero-shot, embodiment-aware deployment. It operates in Cartesian space during policy learning and lifts inference-time diffusion sampling into the target robot's joint space using forward kinematics and Jacobian-based updates. The method improves task success and reduces collisions across simulated and physical robots.
From arXiv Robotics — research abstracts
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Environment: simulation
“ole-body collision-aware guidance over joint trajectories after each denoising step, the arm can be steered away from collisions while preserving learned end-effector behavior. Across 9 simulated robots, EmbodiSteer reduces collision rate by 46.1 percentage points and increases task success by 28.5 percentage points over Cartesian-only execution; on two physical robots in highly constrained scenarios, the respective”
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- 2026-10-30T23:03:19.207Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T23:03:08.118Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalEmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment
EmbodiSteer is a training-free framework that steers embodiment-agnostic visuomotor policies toward zero-shot, embodiment-aware deployment. It operates in Cartesian space during policy learning and lifts inference-time diffusion sampling into the target robot's joint space using forward kinematics and Jacobian-based updates. The method improves task success and reduces collisions across simulated and physical robots.
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