カーネル化運動プリミティブを用いた不確実性を考慮した模倣学習
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
カーネル化運動プリミティブ(KMP)でロボット動作のばらつき・相関・不確実性を予測し、最適制御器融合と組み合わせることで、不確実な状況では安全に振る舞い、部分的な実演から複雑なタスクを最適に遂行できる模倣学習手法を提案した。
著者: João Silvério, Yanlong Huang, Fares J. Abu-Dakka, Leonel Rozo, Darwin G. Caldwell
分類: cs.RO, cs.LG
原文アブストラクト
During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the literature. One of their most prominent features, in addition to extracting a mean trajectory from task demonstrations, is that they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty about robot actions. This rich set of information is used in combination with optimal controller fusion to learn actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that using our approach the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains.
