Motion Policy Networks

Adam Fishman, Adithyavairavan Murali, Clemens Eppner, Bryan Peele, Byron Boots, Dieter Fox

Keywords: Motion Control, Imitation Learning, End-to-End Learning

TL;DR: Motion Policy Networks are trained on millions of example trajectories to generate collision-free, smooth motion from just a single depth camera image

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 more than 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. Videos and code are available at https://mpinets.github.io

Student First Author: yes

Website: https://mpinets.github.io/

Code: https://github.com/nvlabs/motion-policy-networks