やったことではなく望むことを：行動系列計画による技能の模倣
Do What I Want, Not What I Did: Imitation of Skills by Planning Sequences of Actions
専門家のデモから高レベル行動を獲得し、サンプリングベースの運動計画で新環境に適応した行動系列を選ぶ学習手法を提案。磁気構造組立タスクで障害物がある環境でも適切に行動選択できることを示した。
著者: Chris Paxton, Felix Jonathan, Marin Kobilarov, Gregory D Hager
分類: cs.RO
原文アブストラクト
We propose a learning-from-demonstration approach for grounding actions from expert data and an algorithm for using these actions to perform a task in new environments. Our approach is based on an application of sampling-based motion planning to search through the tree of discrete, high-level actions constructed from a symbolic representation of a task. Recursive sampling-based planning is used to explore the space of possible continuous-space instantiations of these actions. We demonstrate the utility of our approach with a magnetic structure assembly task, showing that the robot can intelligently select a sequence of actions in different parts of the workspace and in the presence of obstacles. This approach can better adapt to new environments by selecting the correct high-level actions for the particular environment while taking human preferences into account.
