Human-Robot Gym: Benchmarking Reinforcement Learning in Human-Robot Collaboration

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. We provide challenging, realistic HRC tasks in a modular simulation framework. Most importantly, human-robot gym is the first benchmark suite that includes a safety shield to provably guarantee human safety. 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) by leveraging expert knowledge in form of an action imitation reward, the RL agent can outperform the expert, and (c) our agents negligibly overfit to training data.