Publikation
Extended Tree Search for Robot Task and Motion Planning
Tianyu Ren; Georgia Chalvatzaki; Jan Peters
In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024, Abu Dhabi, United Arab Emirates, October 14-18, 2024. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pages 12048-12055, IEEE, 2024.
Zusammenfassung
Integrated task and motion planning (TAMP) is
desirable for generalized autonomy robots but it is challenging
at the same time. TAMP requires the planner to not only
search in both the large symbolic task space and the high-
dimension motion space but also deal with the infeasible task
actions due to its intrinsic hierarchical process. We propose a
novel decision-making framework for TAMP by constructing
an extended decision tree for both symbolic task planning and
high-dimension motion variable binding. We integrate top-k
planning for generating explicitly a skeleton space where a
variety of candidate skeleton plans are at disposal. Moreover,
we effectively combine this skeleton space with the resultant
motion variable spaces into a single extended decision space.
Accordingly, we use Monte-Carlo Tree Search (MCTS) to ensure
an exploration-exploitation balance at each decision node and
optimize globally to produce optimal solutions. The proposed
seamless combination of symbolic top-k planning with streams,
with the proved optimality of MCTS, leads to a powerful
planning algorithm that can handle the combinatorial complexity
of long-horizon manipulation tasks. We empirically evaluate our
proposed algorithm in challenging robot tasks with different
domains that require multi-stage decisions and show how our
method can overcome the large task space and motion space
through its effective tree search compared to its most competitive
baseline method.