Learning Control Policies to Provably Satisfy Hard Affine Constraints for Black-Box Hybrid Dynamical Systems

arXiv:2604.22244 · cs.RO · Submitted 2026-04-24 · Read on arXiv

cs.RO

Submitted: 2026-04-24

Updated: 2026-09-26

Terminology

• Hybrid System — 35× in this paper · explained in 2 episodes
• Actor — 28× in this paper · explained in 3 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Model Predictive Control — 4× in this paper
• Reward Shaping — 4× in this paper · explained in 2 episodes
• APEX — 3× in this paper · explained in 1 episode
• Constrained Policy Optimization — 3× in this paper
• Activation Function — 2× in this paper · explained in 1 episode
• Control Barrier Functions (CBFs) — 2× in this paper · explained in 4 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Comparison of the Average Constraint Satisfaction — 1× in this paper
• IEEE Conference on Decision and Control — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• International Conference on Robotics and Automation — 1× in this paper
• Model Predictive Control (MPC) — 1× in this paper · explained in 5 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes

Sources

• State-wise Constrained Policy Optimization
• Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization
• A Review of Safe Reinforcement Learning: Methods, Theory and Applications

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