Learning Constraints From Locally-Optimal Demonstrations Under Cost Function Uncertainty
Learning Constraints From Locally-Optimal Demonstrations Under Cost Function Uncertainty
复制标题
成本函数不确定性下的局部最优演示的学习约束
DOI：
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复制
发表时间：
2020
影响因子：
5.2
通讯作者：
D. Berenson
中科院分区：
文献类型：
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作者：
Glen Chou;N. Ozay;D. Berenson
我们提出了一种从局部最优演示中学习参数约束的算法，其中被优化的成本函数对于学习者来说是不确定的。我们的方法在混合整数线性规划（MILP）中利用演示的卡罗需 - 库恩 - 塔克（KKT）最优性条件来学习与演示的局部最优性一致的约束，要么使用已知的约束参数化，要么通过逐步增加与演示一致的参数化。我们对恢复的安全/不安全集合的保守性提供了理论保证，并分析了使用局部最优演示时约束可学习性的极限。我们通过对7自由度手臂和四旋翼示例学习约束，在高维约束和系统上评估了我们的方法，表明它优于竞争的约束学习方法，并且可以有效地用于在环境中规划新的满足约束的轨迹。
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.
DOI：
10.1177/02783649211035177
发表时间：
2021
期刊：
The International Journal of Robotics Research
影响因子：
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作者：
Chou, Glen;Berenson, Dmitry;Ozay, Necmiye
通讯作者：
Ozay, Necmiye
DOI：
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发表时间：
2019
期刊：
Conference on Robot Learning (CoRL
影响因子：
--
作者：
Chou, Glen;Ozay, Necmiye;Berenson, Dmitry
通讯作者：
Berenson, Dmitry