人間とロボットの協働を支える適応型サポートシステム
How to be Helpful? Supportive Behaviors and Personalization for Human-Robot Collaboration
人間とロボットが共有する階層的タスクモデルから部分観測マルコフモデルを構築し、オンラインMonte-Carlo求解で短期的なロボット実行計画を生成することで、限られた操作能力や不完全な環境認識のもとでも人間を支援できる協働システムを提案した。
著者: Olivier Mangin, Alessandro Roncone, Brian Scassellati
分類: cs.RO
原文アブストラクト
The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in recent years. Thanks in part to advances in control and perception algorithms, robots have started to work in increasingly unstructured environments, where they operate side by side with humans to achieve shared tasks. However, little progress has been made toward the development of systems that are truly effective in supporting the human, proactive in their collaboration, and that can autonomously take care of part of the task. In this work, we present a collaborative system capable of assisting a human worker despite limited manipulation capabilities, incomplete model of the task, and partial observability of the environment. Our framework leverages information from a high-level, hierarchical model that is shared between the human and robot and that enables transparent synchronization between the peers and mutual understanding of each other's plan. More precisely, we firstly derive a partially observable Markov model from the high-level task representation; we then use an online Monte-Carlo solver to compute a short-horizon robot-executable plan. The resulting policy is capable of interactive replanning on-the-fly, dynamic error recovery, and identification of hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a realistic furniture construction task.
