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2026-02-13 09:14:02
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Motion Policy Networks Motion Policy Networks Adam Fishman 1,2 , Adithyavairan Murali 2 , Clemens Eppner 2 , Bryan Peele 2 , Byron Boots 1,2 , Dieter Fox 1,2 1 University of Washington, 2 NVIDIA Paper Code Data Abstract Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not only should the solutions be optimal, the motion generator itself must be fast enough for real-time performance and reliable enough for practical deployment. A wide variety of methods have been proposed ranging from local controllers to global planners, often being combined to offset their shortcomings. We present an end-to-end neural model called Motion Policy Networks (MπNets) to generate collision-free, smooth motion from just a single depth camera observation. MπNets are trained on over 3 million motion planning problems in over 500,000 environments. Our...
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