人間のフィードバックから学ぶ動的なロボットから人間への物体受け渡し
Learning Dynamic Robot-to-Human Object Handover from Human Feedback
ロボットが人間と対話しながら物体の受け渡しを学習する手法を提案し、水筒の受け渡し実験で人間の動きに適応できることを示した。
著者: Andras Kupcsik, David Hsu, Wee Sun Lee
分類: cs.RO
原文アブストラクト
Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing workshops. It appears deceptively simple, as humans perform object handover almost flawlessly. The success of humans, however, belies the complexity of object handover as collaborative physical interaction between two agents with limited communication. This paper presents a learning algorithm for dynamic object handover, for example, when a robot hands over water bottles to marathon runners passing by the water station. We formulate the problem as contextual policy search, in which the robot learns object handover by interacting with the human. A key challenge here is to learn the latent reward of the handover task under noisy human feedback. Preliminary experiments show that the robot learns to hand over a water bottle naturally and that it adapts to the dynamics of human motion. One challenge for the future is to combine the model-free learning algorithm with a model-based planning approach and enable the robot to adapt over human preferences and object characteristics, such as shape, weight, and surface texture.
