论文标题
在成本功能不确定性下，从本地最佳示威的学习限制
Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty
论文作者
论文摘要
我们提出了一种从本地最佳演示中学习参数约束的算法，其中要优化的成本函数对学习者而言是不确定的。我们的方法使用混合整数线性程序（MILP）中演示的Karush-Kuhn-Tucker（KKT）最佳条件来学习与演示的局部最优性，通过使用已知约束参数化或通过增量增长的参数化来与演示一致的约束。我们提供理论保证，以保守恢复的安全/不安全集的保守性，并在使用本地最佳示范时分析约束可学习的限制。我们通过学习7-DOF ARM和四型示例的学习约束来评估我们的方法对高维约束和系统，表明它表现优于竞争性的约束学习方法，并可以有效地用于计划环境中的新约束可满足的轨迹。
We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner. Our method uses the Karush-Kuhn-Tucker (KKT) optimality conditions of the demonstrations within a mixed integer linear program (MILP) to learn constraints which are consistent with the local optimality of the demonstrations, by either using a known constraint parameterization or by incrementally growing a parameterization that is consistent with the demonstrations. We provide theoretical guarantees on the conservativeness of the recovered safe/unsafe sets and analyze the limits of constraint learnability when using locally-optimal demonstrations. We evaluate our method on high-dimensional constraints and systems by learning constraints for 7-DOF arm and quadrotor examples, show that it outperforms competing constraint-learning approaches, and can be effectively used to plan new constraint-satisfying trajectories in the environment.
