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：推断未知约束是许多机器人应用中的一个具有挑战性且关键的问题。当仅提供专家演示时，推断未知的领域约束对于有效部署额外智能体至关重要。本文提出了一种方法，在观察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建模为一个逆优化问题，并提出了一种交替优化方案，通过最小化KKT残差目标来推断未知约束。我们通过多项仿真实验证明了该方法的有效性，并表明与近期基线方法相比，我们的方法能够推断出更不保守的约束，同时保持相当的安全保障。