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Rearrangement-based nonprehensile manipulation still remains as a challengingproblem due to the high-dimensional problem space and the complex physicaluncertainties it entails. We formulate this class of problems as a coupledproblem of local rearrangement and global action optimization by incorporatingfree-space transit motions between constrained rearranging actions. We proposea forest-based kinodynamic planning framework to concurrently search inmultiple problem regions, so as to enable global exploration of the mosttask-relevant subspaces, while facilitating effective switches between localrearranging actions. By interleaving dynamic horizon planning and actionexecution, our framework can adaptively handle real-world uncertainties. Withextensive experiments, we show that our framework significantly improves theplanning efficiency and manipulation effectiveness while being robust againstvarious uncertainties.

Kinodynamic Rapidly-exploring Random Forest for Rearrangement-Based Nonprehensile Manipulation