家庭用サービスロボットへの動作観察学習の応用：三つの常識の定式化
Applying Learning-from-observation to household service robots: three common-sense formulation
家庭内タスクの動作観察学習において、ラバノーテーション・接触ウェブ・物理的/意味的制約という三つの常識表現を用いてタスクモデルを生成し、ロボットで実行する枠組みを提案した。
著者: Katsushi Ikeuchi, Jun Takamatsu, Kazuhiro Sasabuchi, Naoki Wake, Atsushi Kanehiro
分類: cs.RO
原文アブストラクト
Utilizing a robot in a new application requires the robot to be programmed at each time. To reduce such programmings efforts, we have been developing ``Learning-from-observation (LfO)'' that automatically generates robot programs by observing human demonstrations. One of the main issues with introducing this LfO system into the domain of household tasks is the cluttered environments, which cause difficulty in determining which elements are important for task execution when observing demonstrations. To overcome this issue, it is necessary for the system to have common sense shared with the human demonstrator. This paper addresses three relationships that LfO in the household domain should focus on when observing demonstrations and proposes representations to describe the common sense used by the demonstrator for optimal execution of task sequences. Specifically, the paper proposes to use labanotation to describe the postures between the environment and the robot, contact-webs to describe the grasping methods between the robot and the tool, and physical and semantic constraints to describe the motions between the tool and the environment. Then, based on these representations, the paper formulates task models, machine-independent robot programs, that indicate what to do and how to do. Third, the paper explains the task encoder to obtain task models and task decoder to execute the task models on the robot hardware. Finally, this paper presents how the system actually works through several example scenes.
