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至关重要的问题。当仅有专家示范可用时，推断未知的领域约束对于有效部署其他智能体变得必不可少。本文提出一种方法，在观察专家示范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残差目标来推断未知约束。我们在多个仿真中验证了该方法的有效性，并证明与近期基线方法相比，我们的方法能在保持相当安全保证的同时推断出更保守的约束。