  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.
