深層強化学習による集団への社会的に適切なロボット接近行動の学習
Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning
シミュレーションで段階的に学習したロボットの接近行動モデルを実機に適用し、人間の小集団への接近が社会的に適切になることを被験者実験で示した。
著者: Yuan Gao, Fangkai Yang, Martin Frisk, Daniel Hernandez, Christopher Peters, Ginevra Castellano
分類: cs.RO, cs.HC, cs.LG
原文アブストラクト
Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge. In this paper, we present a deep learning scheme that acquires a prior model of robot approaching behavior in simulation and applies it to real-world interaction with a physical robot approaching groups of humans. The scheme, which we refer to as Staged Social Behavior Learning (SSBL), considers different stages of learning in social scenarios. We learn robot approaching behaviors towards small groups in simulation and evaluate the performance of the model using objective and subjective measures in a perceptual study and a HRI user study with human participants. Results show that our model generates more socially appropriate behavior compared to a state-of-the-art model.
