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In recent years, both reinforcement learning and learning-based control -- aswell as the study of their safety, which is crucial for deployment inreal-world robots -- have gained significant traction. However, to adequatelygauge the progress and applicability of new results, we need the tools toequitably compare the approaches proposed by the controls and reinforcementlearning communities. Here, we propose a new open-source benchmark suite,called safe-control-gym, supporting both model-based and data-based controltechniques. We provide implementations for three dynamic systems -- thecart-pole, the 1D, and 2D quadrotor -- and two control tasks -- stabilizationand trajectory tracking. We propose to extend OpenAI's Gym API -- the de factostandard in reinforcement learning research -- with (i) the ability to specify(and query) symbolic dynamics and (ii) constraints, and (iii) (repeatably)inject simulated disturbances in the control inputs, state measurements, andinertial properties. To demonstrate our proposal and in an attempt to bringresearch communities closer together, we show how to use safe-control-gym toquantitatively compare the control performance, data efficiency, and safety ofmultiple approaches from the fields of traditional control, learning-basedcontrol, and reinforcement learning.

safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics