  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.
