人間と協働するロボットのためのマルチモーダル強化学習
Multimodal Reinforcement Learning for Robots Collaborating with Humans
人間と協働するロボットの対話・行動方針を、言語と身体動作を含むマルチモーダル強化学習で学習する手法を提案し、実ユーザとの実験で有効性を確認した。
著者: Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul, Bahareh Abbasi, Barbara Di Eugenio, Milos Zefran
分類: cs.RO
原文アブストラクト
Robot assistants for older adults and people with disabilities need to interact with their users in collaborative tasks. The core component of these systems is an interaction manager whose job is to observe and assess the task, and infer the state of the human and their intent to choose the best course of action for the robot. Due to the sparseness of the data in this domain, the policy for such multi-modal systems is often crafted by hand; as the complexity of interactions grows this process is not scalable. In this paper, we propose a reinforcement learning (RL) approach to learn the robot policy. In contrast to the dialog systems, our agent is trained with a simulator developed by using human data and can deal with multiple modalities such as language and physical actions. We conducted a human study to evaluate the performance of the system in the interaction with a user. Our designed system shows promising preliminary results when it is used by a real user.
