论文标题
通过从次优的演示中学习时间逻辑公式来解释多阶段任务
Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations
论文作者
论文摘要
我们提出了一种通过学习一致的线性时间逻辑（LTL）公式的逻辑结构和原子命题来学习多阶段任务的方法。学习者获得了成功但潜在的次优示范，在这种情况下，演示者在满足LTL公式的同时优化了成本函数，而成本函数对学习者来说是不确定的。我们的算法使用了演示的Karush-Kuhn-Tucker（KKT）最佳条件以及反例引导的伪造策略，分别学习原子主词参数和LTL公式的逻辑结构。我们提供了有关回收原子命题集的保守性的理论保证，以及在寻找与示范一致的LTL公式的完整性中。我们通过学习LTL公式来评估高维非线性系统的方法，该公式解释了7-DOF ARM和四极管系统的多阶段任务，并表明它优于从积极示例中学习LTL公式的竞争方法。
We present a method for learning multi-stage tasks from demonstrations by learning the logical structure and atomic propositions of a consistent linear temporal logic (LTL) formula. The learner is given successful but potentially suboptimal demonstrations, where the demonstrator is optimizing a cost function while satisfying the LTL formula, and the cost function is uncertain to the learner. Our algorithm uses the Karush-Kuhn-Tucker (KKT) optimality conditions of the demonstrations together with a counterexample-guided falsification strategy to learn the atomic proposition parameters and logical structure of the LTL formula, respectively. We provide theoretical guarantees on the conservativeness of the recovered atomic proposition sets, as well as completeness in the search for finding an LTL formula consistent with the demonstrations. We evaluate our method on high-dimensional nonlinear systems by learning LTL formulas explaining multi-stage tasks on 7-DOF arm and quadrotor systems and show that it outperforms competing methods for learning LTL formulas from positive examples.
