Motion Policy Networks

• URL: http://arxiv.org/abs/2210.12209v1
• Date: Fri, 21 Oct 2022 19:37:09 GMT
• Title: Motion Policy Networks
• Authors: Adam Fishman, Adithyavairan Murali, Clemens Eppner, Bryan Peele, Byron Boots, Dieter Fox
• Abstract summary: We present an end-to-end neural model called Motion Policy Networks (M$pi$Nets) to generate collision-free, smooth motion from a single depth camera observation. Our experiments show that M$pi$Nets are significantly faster than global planners while exhibiting the reactivity needed to deal with dynamic scenes.
• Score: 61.87789591369106
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 (M$\pi$Nets) to generate collision-free, smooth motion from just a single depth camera observation. M$\pi$Nets are trained on over 3 million motion planning problems in over 500,000 environments. Our experiments show that M$\pi$Nets are significantly faster than global planners while exhibiting the reactivity needed to deal with dynamic scenes. They are 46% better than prior neural planners and more robust than local control policies. Despite being only trained in simulation, M$\pi$Nets transfer well to the real robot with noisy partial point clouds. Code and data are publicly available at https://mpinets.github.io.

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