Representation, learning, and planning algorithms for geometric task and motion planning. (arXiv:2203.04605v1 [cs.RO])

We present a framework for learning to guide geometric task and motion
planning (GTAMP). GTAMP is a subclass of task and motion planning in which the
goal is to move multiple objects to target regions among movable obstacles. A
standard graph search algorithm is not directly applicable, because GTAMP
problems involve hybrid search spaces and expensive action feasibility checks.
To handle this, we introduce a novel planner that extends basic heuristic
search with random sampling and a heuristic function that …

algorithms arxiv learning motion planning planning representation