データ効率の良い条件付き行動予測による先回り安全な人とロボットの協調
Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction
データが少ない環境でも、ロボットの将来行動に依存する人間の条件付きモデルを構築し、先回り的な目標選択と安全な軌道生成を実現する手法を提案した。
著者: Ravi Pandya, Zhuoyuan Wang, Yorie Nakahira, Changliu Liu
分類: cs.RO
原文アブストラクト
We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting data is sparse. Much prior work in human-robot collaboration uses observational models of humans (i.e. models that treat the robot purely as an observer) to choose the robot's behavior, but such models do not account for the influence the robot has on the human's actions, which may lead to inefficient interactions. We instead formulate the problem of optimally choosing a collaborative robot's behavior based on a conditional model of the human that depends on the robot's future behavior. First, we propose a novel model-based formulation of conditional behavior prediction that allows the robot to infer the human's intentions based on its future plan in data-sparse environments. We then show how to utilize a conditional model for proactive goal selection and safe trajectory generation around human collaborators. Finally, we use our proposed proactive controller in a collaborative task with real users to show that it can improve users' interactions with a robot collaborator quantitatively and qualitatively.
