将来のタスクを先読みするタスク・動作計画
Anticipatory Task and Motion Planning
将来のタスクのコストを学習モデルで予測し、後続タスクの妨げになる副作用を避けるように計画を選ぶ手法を提案。シミュレーションと実機で効果を検証した。
著者: Roshan Dhakal, Duc M. Nguyen, Tom Silver, Xuesu Xiao, Gregory J. Stein
分類: cs.RO
原文アブストラクト
We consider a sequential task and motion planning (tamp) setting in which a robot is assigned continuous-space rearrangement-style tasks one-at-a-time in an environment that persists between each. Lacking advance knowledge of future tasks, existing (myopic) planning strategies unwittingly introduce side effects that impede completion of subsequent tasks: e.g., by blocking future access or manipulation. We present anticipatory task and motion planning, in which estimates of expected future cost from a learned model inform selection of plans generated by a model-based tamp planner so as to avoid such side effects, choosing configurations of the environment that both complete the task and minimize overall cost. Simulated multi-task deployments in navigation-among-movable-obstacles and cabinet-loading domains yield improvements of 32.7% and 16.7% average per-task cost respectively. When given time in advance to prepare the environment, our learning-augmented planning approach yields improvements of 83.1% and 22.3%. Both showcase the value of our approach. Finally, we also demonstrate anticipatory tamp on a real-world Fetch mobile manipulator.
