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Inferring unknown constraints is a challenging and crucial problem in manyrobotics applications. When only expert demonstrations are available, itbecomes essential to infer the unknown domain constraints to deploy additionalagents effectively. In this work, we propose an approach to infer affineconstraints in control tasks after observing expert demonstrations. Weformulate the constraint inference problem as an inverse optimization problem,and we propose an alternating optimization scheme that infers the unknownconstraints by minimizing a KKT residual objective. We demonstrate theeffectiveness of our method in a number of simulations, and show that ourmethod can infer less conservative constraints than a recent baseline method,while maintaining comparable safety guarantees.

Constraint Inference in Control Tasks from Expert Demonstrations via Inverse Optimization