Whole-Body Neural Policy for Zero-Shot Cross-Embodiment Motion Planning
Keywords: Learning to Plan, Generalization, Motion Planning
TL;DR: We present a novel configuration-space neural motion policy for re-targeting planning behavior zero-shot to unseen robotic manipulators.
Abstract: Classical manipulator motion planners work across different robot embodiments. However they plan on a pre-specified static environment representation, and are not scalable to unseen dynamic environments. Neural Motion Planners (NMPs) are an appealing alternative to conventional planners as they incorporate different environmental constraints to learn motion policies directly from raw sensor observations. Contemporary state-of-the-art NMPs can successfully plan across different environments. However none of the existing NMPs generalize across robot embodiments. In this paper we propose Cross-Embodiment Motion Policy (XMoP), a neural policy for learning to plan over a distribution of manipulators. XMoP implicitly learns to satisfy kinematic constraints for a distribution of robots and $\textit{zero-shot}$ transfers the planning behavior to unseen robotic manipulators within this distribution. We trained XMoP on planning demonstrations from over three million procedurally sampled robotic manipulators in different simulated environments. Despite being completely trained on synthetic embodiments and environments, our policy exhibits strong sim-to-real generalization across manipulators with different kinematic variations and degrees of freedom with the same set of frozen policy parameters. We show sim-to-real demonstrations on two unseen manipulators solving novel planning problems in different real-world environments even with dynamic obstacles. Videos are available at https://sites.google.com/view/xmop.
Submission Number: 11
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