Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics

Ajinkya Jain, Scott Niekum
Proceedings of The 2nd Conference on Robot Learning, PMLR 87:757-766, 2018.

Abstract

Noisy observations coupled with nonlinear dynamics pose one of the biggestchallengesinrobotmotionplanning. Bydecomposingnonlineardynamics into a discrete set of local dynamics models, hybrid dynamics provide a natural way to model nonlinear dynamics, especially in systems with sudden discontinuities in dynamics due to factors such as contacts. We propose a hierarchical POMDP planner that develops cost-optimized motion plans for hybrid dynamics models. The hierarchical planner first develops a high-level motion plan to sequence the local dynamics models to be visited and then converts it into a detailed continuous state plan. This hierarchical planning approach results in a decomposition of the POMDP planning problem into smaller sub-parts that can be solved with significantly lower computational costs. The ability to sequence the visitation of local dynamics models also provides a powerful way to leverage the hybrid dynamics to reduce state uncertainty. We evaluate the proposed planner on a navigation task in the simulated domain and on an assembly task with a robotic manipulator, showing that our approach can solve tasks having high observation noise and nonlinear dynamics effectively with significantly lower computational costs compared to direct planning approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v87-jain18a, title = {Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics}, author = {Jain, Ajinkya and Niekum, Scott}, booktitle = {Proceedings of The 2nd Conference on Robot Learning}, pages = {757--766}, year = {2018}, editor = {Billard, Aude and Dragan, Anca and Peters, Jan and Morimoto, Jun}, volume = {87}, series = {Proceedings of Machine Learning Research}, month = {29--31 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v87/jain18a/jain18a.pdf}, url = {https://proceedings.mlr.press/v87/jain18a.html}, abstract = {Noisy observations coupled with nonlinear dynamics pose one of the biggestchallengesinrobotmotionplanning. Bydecomposingnonlineardynamics into a discrete set of local dynamics models, hybrid dynamics provide a natural way to model nonlinear dynamics, especially in systems with sudden discontinuities in dynamics due to factors such as contacts. We propose a hierarchical POMDP planner that develops cost-optimized motion plans for hybrid dynamics models. The hierarchical planner first develops a high-level motion plan to sequence the local dynamics models to be visited and then converts it into a detailed continuous state plan. This hierarchical planning approach results in a decomposition of the POMDP planning problem into smaller sub-parts that can be solved with significantly lower computational costs. The ability to sequence the visitation of local dynamics models also provides a powerful way to leverage the hybrid dynamics to reduce state uncertainty. We evaluate the proposed planner on a navigation task in the simulated domain and on an assembly task with a robotic manipulator, showing that our approach can solve tasks having high observation noise and nonlinear dynamics effectively with significantly lower computational costs compared to direct planning approaches. } }
Endnote
%0 Conference Paper %T Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics %A Ajinkya Jain %A Scott Niekum %B Proceedings of The 2nd Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2018 %E Aude Billard %E Anca Dragan %E Jan Peters %E Jun Morimoto %F pmlr-v87-jain18a %I PMLR %P 757--766 %U https://proceedings.mlr.press/v87/jain18a.html %V 87 %X Noisy observations coupled with nonlinear dynamics pose one of the biggestchallengesinrobotmotionplanning. Bydecomposingnonlineardynamics into a discrete set of local dynamics models, hybrid dynamics provide a natural way to model nonlinear dynamics, especially in systems with sudden discontinuities in dynamics due to factors such as contacts. We propose a hierarchical POMDP planner that develops cost-optimized motion plans for hybrid dynamics models. The hierarchical planner first develops a high-level motion plan to sequence the local dynamics models to be visited and then converts it into a detailed continuous state plan. This hierarchical planning approach results in a decomposition of the POMDP planning problem into smaller sub-parts that can be solved with significantly lower computational costs. The ability to sequence the visitation of local dynamics models also provides a powerful way to leverage the hybrid dynamics to reduce state uncertainty. We evaluate the proposed planner on a navigation task in the simulated domain and on an assembly task with a robotic manipulator, showing that our approach can solve tasks having high observation noise and nonlinear dynamics effectively with significantly lower computational costs compared to direct planning approaches.
APA
Jain, A. & Niekum, S.. (2018). Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics. Proceedings of The 2nd Conference on Robot Learning, in Proceedings of Machine Learning Research 87:757-766 Available from https://proceedings.mlr.press/v87/jain18a.html.

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