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Noisy observations coupled with nonlinear dynamics pose one of the biggestchallenges in robot motion planning. By decomposing nonlinear dynamics into adiscrete set of local dynamics models, hybrid dynamics provide a natural way tomodel nonlinear dynamics, especially in systems with sudden discontinuities indynamics due to factors such as contacts. We propose a hierarchical POMDPplanner that develops cost-optimized motion plans for hybrid dynamics models.The hierarchical planner first develops a high-level motion plan to sequencethe local dynamics models to be visited and then converts it into a detailedcontinuous state plan. This hierarchical planning approach results in adecomposition of the POMDP planning problem into smaller sub-parts that can besolved with significantly lower computational costs. The ability to sequencethe visitation of local dynamics models also provides a powerful way toleverage the hybrid dynamics to reduce state uncertainty. We evaluate theproposed planner on a navigation task in the simulated domain and on anassembly task with a robotic manipulator, showing that our approach can solvetasks having high observation noise and nonlinear dynamics effectively withsignificantly lower computational costs compared to direct planning approaches.

Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics