アシスティブ・ジム：介助ロボティクス向け物理シミュレーション基盤
Assistive Gym: A Physics Simulation Framework for Assistive Robotics
日常生活動作の介助を6種類の環境で模擬できるオープンソース物理シミュレーションフレームワークを構築し、強化学習によるベースライン方策と複数ロボットの性能比較を示した。
著者: Zackory Erickson, Vamsee Gangaram, Ariel Kapusta, C. Karen Liu, Charles C. Kemp
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people worldwide. Yet, conducting research in this area presents numerous challenges, including the risks of physical interaction between people and robots. Physics simulations have been used to optimize and train robots for physical assistance, but have typically focused on a single task. In this paper, we present Assistive Gym, an open source physics simulation framework for assistive robots that models multiple tasks. It includes six simulated environments in which a robotic manipulator can attempt to assist a person with activities of daily living (ADLs): itch scratching, drinking, feeding, body manipulation, dressing, and bathing. Assistive Gym models a person's physical capabilities and preferences for assistance, which are used to provide a reward function. We present baseline policies trained using reinforcement learning for four different commercial robots in the six environments. We demonstrate that modeling human motion results in better assistance and we compare the performance of different robots. Overall, we show that Assistive Gym is a promising tool for assistive robotics research.
