Robot Skill Learning with Identification of Preconditions and Postconditions via Level Set Estimation

摘要

Hierarchical algorithms have often been used to plan and execute complicated robotic sequential manipulation tasks, where an abstract planner searches for a skill sequence in an abstract space, and each skill generates actual motions on the basis of the planned skill sequences. To generate executable plans, the abstract planner should know the pre-/postconditions of each skill and appropriately choose skills so that the generated plan satisfies their pre-/postconditions. For such hierarchical planning, this paper presents a novel method for robot skill learning that learns not only a control policy but also the learned skill's pre-/postconditions to complete a given task. Our method combines an optimal control method and an active learning approach called level set estimation (LSE) to effectively collect training data for learning control policies and pre-/postconditions. Although there exists a LSE-based policy learning algorithm that identifies preconditions, its performance is limited to cases where the dimension of the search space for pre-/postconditions is low. The main contribution of this paper is the proposal of a new learning method that can handle tasks having a high-dimensional search space for pre-/postconditions. We demonstrate our proposed method in two robotic tasks. The results show that our method can more effectively learn a control policy and its pre-/postconditions compared with the existing LSE-based method.