人間-ロボット協調における安全で効率的な人間モデル探索
Safe and Efficient Exploration of Human Models During Human-Robot Interaction
ロボットが人間のモデルを安全に探索して精度を高める手法を提案し、エネルギー関数ベースの安全制御下で探索戦略の違いやモデル種別への影響を分析した。
著者: Ravi Pandya, Changliu Liu
分類: cs.RO
原文アブストラクト
Many collaborative human-robot tasks require the robot to stay safe and work efficiently around humans. Since the robot can only stay safe with respect to its own model of the human, we want the robot to learn a good model of the human in order to act both safely and efficiently. This paper studies methods that enable a robot to safely explore the space of a human-robot system to improve the robot's model of the human, which will consequently allow the robot to access a larger state space and better work with the human. In particular, we introduce active exploration under the framework of energy-function based safe control, investigate the effect of different active exploration strategies, and finally analyze the effect of safe active exploration on both analytical and neural network human models.
