Human-Robot Gym：人とロボットの協調における強化学習のベンチマーク
Human-Robot Gym: Benchmarking Reinforcement Learning in Human-Robot Collaboration
人とロボットの協調(HRC)における安全な強化学習のためのベンチマークスイートを提案し、安全性を保証するシールドを備えた8つのタスクで最先端手法を評価した。
著者: Jakob Thumm, Felix Trost, Matthias Althoff
分類: cs.RO
原文アブストラクト
Deep reinforcement learning (RL) has shown promising results in robot motion planning with first attempts in human-robot collaboration (HRC). However, a fair comparison of RL approaches in HRC under the constraint of guaranteed safety is yet to be made. We, therefore, present human-robot gym, a benchmark suite for safe RL in HRC. Our benchmark suite provides eight challenging, realistic HRC tasks in a modular simulation framework. Most importantly, human-robot gym includes a safety shield that provably guarantees human safety. We are, thereby, the first to provide a benchmark suite to train RL agents that adhere to the safety specifications of real-world HRC. This bridges a critical gap between theoretic RL research and its real-world deployment. Our evaluation of six tasks led to three key results: (a) the diverse nature of the tasks offered by human-robot gym creates a challenging benchmark for state-of-the-art RL methods, (b) incorporating expert knowledge in RL training in the form of an action-based reward can outperform the expert, and (c) our agents negligibly overfit to training data.
