共有自律制御におけるフィードバックからの人間の制約のオンライン学習
Online Learning of Human Constraints from Feedback in Shared Autonomy
共有自律制御において、人間の作業負荷と不快感を軽減するため、フィードバックから人間の身体的制約をオンラインで学習し、人間の人間工学的な好みや限界に合わせて動作を適応させる支援エージェントを提案した。
著者: Shibei Zhu, Tran Nguyen Le, Samuel Kaski, Ville Kyrki
分類: cs.RO, cs.HC, cs.LG
原文アブストラクト
Real-time collaboration with humans poses challenges due to the different behavior patterns of humans resulting from diverse physical constraints. Existing works typically focus on learning safety constraints for collaboration, or how to divide and distribute the subtasks between the participating agents to carry out the main task. In contrast, we propose to learn a human constraints model that, in addition, considers the diverse behaviors of different human operators. We consider a type of collaboration in a shared-autonomy fashion, where both a human operator and an assistive robot act simultaneously in the same task space that affects each other's actions. The task of the assistive agent is to augment the skill of humans to perform a shared task by supporting humans as much as possible, both in terms of reducing the workload and minimizing the discomfort for the human operator. Therefore, we propose an augmentative assistant agent capable of learning and adapting to human physical constraints, aligning its actions with the ergonomic preferences and limitations of the human operator.
