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

翻译：在诸多机器人应用中，推断未知约束是一项具有挑战性且至关重要的问题。当仅能获取专家演示时，推断未知领域约束以有效部署额外智能体变得尤为关键。本文提出一种方法，用于在观测专家演示后推断控制任务中的仿射约束。我们将约束推断问题表述为逆向优化问题，并提出一种交替优化方案，通过最小化KKT残差目标函数来推断未知约束。我们在多项仿真实验中验证了该方法的有效性，结果表明，与近期基线方法相比，本方法能够推断出更少保守性的约束，同时保持相当的安全保障。