人とロボットの協働を学習するデータ効率の良い強化学習フレームワーク
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction
ロボットが自身の感覚運動経験から人間との協働タスクを教師なしで学習し、ガウス過程とベイズ最適化で不確実な人間行動に対処する手法を提案した。
著者: Ali Ghadirzadeh, Judith Bütepage, Atsuto Maki, Danica Kragic, Mårten Björkman
分類: cs.RO, cs.LG
原文アブストラクト
Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-efficient reinforcement learning framework which enables a robot to learn how to collaborate with a human partner. The robot learns the task from its own sensorimotor experiences in an unsupervised manner. The uncertainty of the human actions is modeled using Gaussian processes (GP) to implement action-value functions. Optimal action selection given the uncertain GP model is ensured by Bayesian optimization. We apply the framework to a scenario in which a human and a PR2 robot jointly control the ball position on a plank based on vision and force/torque data. Our experimental results show the suitability of the proposed method in terms of fast and data-efficient model learning, optimal action selection under uncertainties and equal role sharing between the partners.
