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

Rearrangement-based nonprehensile manipula- tion still remains as a challenging problem due to the high- dimensional problem space and the complex physical uncer- tainties it entails. We formulate this class of problems as a coupled problem of local rearrangement and global action op- timization by incorporating free-space transit motions between constrained rearranging actions. We propose a forest-based kinodynamic planning framework to concurrently search in multiple problem regions, so as to enable global exploration of the most task-relevant subspaces, while facilitating effective switches between local rearranging actions. By interleaving dynamic horizon planning and action execution, our framework can adaptively handle real-world uncertainties. With extensive experiments, we show that our framework significantly im- proves the planning efficiency and manipulation effectiveness while being robust against various uncertainties.