safe-control-gym
A unified benchmark suite with PyBullet CartPole and Quadrotor environments and symbolic CasADi dynamics for safe learning-based control and reinforcement learning.
What is safe-control-gym?
safe-control-gym is a Python benchmark suite providing CartPole and Quadrotor environments for developing and testing safe learning-based control and reinforcement learning algorithms. It combines PyBullet physics simulation with CasADi symbolic dynamics to enable rigorous evaluation of safety, robustness, and generalization in robotic control tasks. The project addresses the need for standardized, reproducible benchmarks in safe robotics research.
Researchers and engineers working on safe reinforcement learning, learning-based control, and robotic motion planning who need reproducible benchmarks with symbolic dynamics and safety constraints.
Developers choose safe-control-gym for its unique integration of high-fidelity PyBullet simulation with exact CasADi symbolic models, pre-implemented safety constraints and disturbances, and a comprehensive set of baseline controllers—enabling direct comparison of algorithms in a unified, safety-focused framework.
Overview
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
Use Cases
Best For
- Benchmarking safe reinforcement learning algorithms on robotic tasks
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