触覚フィードバックのための反応的動作モデルを教師あり学習と強化学習で獲得する枠組み
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
タスクのデモを動作プリミティブに分割し、適応動作のデモから初期フィードバックモデルを学習した後、少数の実機試行で強化学習により微調整する枠組みを提案し、人型ロボットの触覚フィードバックタスクで評価した。
著者: Giovanni Sutanto, Katharina Rombach, Yevgen Chebotar, Zhe Su, Stefan Schaal, Gaurav S. Sukhatme, Franziska Meier
分類: cs.RO
原文アブストラクト
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning from demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.
