Motion Policy Networks

Adam Fishman, Adithyavairavan Murali, Clemens Eppner, Bryan Peele, Byron Boots, Dieter Fox
Proceedings of The 6th Conference on Robot Learning, PMLR 205:967-977, 2023.

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 more than 500,000 environments. Our experiments show that MπNets 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, MπNets transfer well to the real robot with noisy partial point clouds. Videos and code are available at https://mpinets.github.io

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-fishman23a, title = {Motion Policy Networks}, author = {Fishman, Adam and Murali, Adithyavairavan and Eppner, Clemens and Peele, Bryan and Boots, Byron and Fox, Dieter}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {967--977}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/fishman23a/fishman23a.pdf}, url = {https://proceedings.mlr.press/v205/fishman23a.html}, 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} }
Endnote
%0 Conference Paper %T Motion Policy Networks %A Adam Fishman %A Adithyavairavan Murali %A Clemens Eppner %A Bryan Peele %A Byron Boots %A Dieter Fox %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-fishman23a %I PMLR %P 967--977 %U https://proceedings.mlr.press/v205/fishman23a.html %V 205 %X 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
APA
Fishman, A., Murali, A., Eppner, C., Peele, B., Boots, B. & Fox, D.. (2023). Motion Policy Networks. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:967-977 Available from https://proceedings.mlr.press/v205/fishman23a.html.

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