Benchmarking Reinforcement Learning for Safe Robotics: Constraints, Robustness and Transfer

Abstract

Safe learning in robotics aims to deploy robots in real life with complex tasks and safety requirements. To push the agenda of safe learning, a crucial step is establishing common benchmarks that facilitate both reliable evaluations and research development. In this work, we contribute towards this goal by surveying the safe learning literature and proposing a versatile safe learning benchmark suite, safe-control-gym. The benchmark implements a variety of safe learning algorithms spanning control to reinforcement learning, it also implements critical features to support safety-relevant evaluations and algorithm development. With safe-control-gym, we conduct careful benchmarking on model-free reinforcement learning methods with respect to three metrics of safety: constraint satisfaction, robustness, and transfer performance. We envision safe-control-gym to provide a framework that brings various research together, and most importantly to accelerate the progress of safe learning in robotics.