人型ロボットの操作動作を対話的に適応させる協調学習
Co-active Learning to Adapt Humanoid Movement for Manipulation
模倣学習で得た動作を様々な環境制約やユーザの好みに合わせて適応させるため、人間参加型の協調学習フレームワークを提案し、人型ロボットで有効性を検証した。
著者: Ren Mao, John S. Baras, Yezhou Yang, Cornelia Fermuller
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
In this paper we address the problem of robot movement adaptation under various environmental constraints interactively. Motion primitives are generally adopted to generate target motion from demonstrations. However, their generalization capability is weak while facing novel environments. Additionally, traditional motion generation methods do not consider the versatile constraints from various users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt robot end-effector's movement for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with various constraints. The framework also considers user's feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. The implemented system generalizes trained motion primitives to various situations with different constraints considering user preferences. Experiments on a humanoid platform validate the effectiveness of our approach.
