REAP: Learning General Rearrangement Heuristics from Motion-Planner Feedback.

arXiv:2610.23973v1 Announce Type: cross Abstract: Robotic rearrangement tasks require planning sequences of actions that fulfill high-level specifications while also being geometrically feasible. Symbolic abstractions make the combinatorial structure of rearra