対話行動を取り入れた人間中心の安全ロボット強化学習フレームワーク
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
ロボット強化学習の安全性を高めるため、安全な探索・価値観の整合・安全な協働の3段階からなる人間中心フレームワークを提案し、対話行動の活用と4つの課題を論じた。
著者: Shangding Gu, Alap Kshirsagar, Yali Du, Guang Chen, Jan Peters, Alois Knoll
分類: cs.RO, cs.AI
原文アブストラクト
Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is a crucial step towards achieving human-robot coexistence. In this paper, we envision a human-centered SRRL framework consisting of three stages: safe exploration, safety value alignment, and safe collaboration. We examine the research gaps in these areas and propose to leverage interactive behaviors for SRRL. Interactive behaviors enable bi-directional information transfer between humans and robots, such as conversational robot ChatGPT. We argue that interactive behaviors need further attention from the SRRL community. We discuss four open challenges related to the robustness, efficiency, transparency, and adaptability of SRRL with interactive behaviors.
