複製を考慮したガウス過程モデルによるタスク適応型ロボットの見まね学習
Task-Adaptive Robot Learning from Demonstration with Gaussian Process Models under Replication
実演データのばらつきとタスクパラメータをガウス過程モデルに組み込み、複製構造を活かして計算コストを抑えつつ、異なる状況に適応できる頑健な方策を学習する手法を提案した。
著者: Miguel Arduengo, Adrià Colomé, Júlia Borràs, Luis Sentis, Carme Torras
分類: cs.RO
原文アブストラクト
Learning from Demonstration (LfD) is a paradigm that allows robots to learn complex manipulation tasks that can not be easily scripted, but can be demonstrated by a human teacher. One of the challenges of LfD is to enable robots to acquire skills that can be adapted to different scenarios. In this paper, we propose to achieve this by exploiting the variations in the demonstrations to retrieve an adaptive and robust policy, using Gaussian Process (GP) models. Adaptability is enhanced by incorporating task parameters into the model, which encode different specifications within the same task. With our formulation, these parameters can be either real, integer, or categorical. Furthermore, we propose a GP design that exploits the structure of replications, i.e., repeated demonstrations with identical conditions within data. Our method significantly reduces the computational cost of model fitting in complex tasks, where replications are essential to obtain a robust model. We illustrate our approach through several experiments on a handwritten letter demonstration dataset.
