Learning Unknown Constraints without Unsafe Data via Optimality and Counterfactual Regularization

The paper introduces Counterfactual KKT (CF‑KKT), a framework that learns unknown constraints from locally optimal demonstrations without requiring known dynamics or risky exploration. CF‑KKT uses a learned differentiable dynamics model to apply KKT‑inspired optimality conditions and generates counterfactual behaviors that reveal infeasible data, enabling safer and more data‑efficient constraint recovery. Experiments on high‑dimensional robotic control tasks show that neural constraint representations learned with CF‑KKT achieve better safety and data efficiency than state‑of‑the‑art offline inverse constrained reinforcement learning baselines.

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• reinforcement-learning
• robotics
• benchmarks
• safety