K. Ren, P. Chanrungmaneekul, L. E. Kavraki, and K. Hang, “Kinodynamic Rapidly-exploring Random Forest for Rearrangement-Based Nonprehensile Manipulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 8127–8133.
Rearrangement-based nonprehensile manipulation still remains as a challenging problem due to the high-dimensional problem space and the complex physical uncertainties it entails. We formulate this class of problems as a coupled problem of local rearrangement and global action optimization 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 improves the planning efficiency and manipulation effectiveness while being robust against various uncertainties.
Publisher: http://dx.doi.org/10.1109/ICRA48891.2023.10161560
PDF preprint: http://kavrakilab.org/publications/ren2023-kinodynamic.pdf