生成による模倣：対話型タスクの模倣のための深層生成モデル
Imitating by generating: deep generative models for imitation of interactive tasks
人間との対話タスクを観察学習と運動感覚学習の組み合わせでロボットに学習させるため、動作埋め込み・相手動作予測・関節軌道生成を組み合わせた深層学習フレームワークを提案し、4種類の対話タスクで有効性を示した。
著者: Judith Bütepage, Ali Ghadirzadeh, Özge Öztimur Karadag, Mårten Björkman, Danica Kragic
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interaction. In this work we want to explore these ideas in a human-robot interaction setting in which a robot is required to learn interactive tasks from a combination of observational and kinesthetic learning. To this end, we propose a deep learning framework consisting of a number of components for (1) human and robot motion embedding, (2) motion prediction of the human partner and (3) generation of robot joint trajectories matching the human motion. To test these ideas, we collect human-human interaction data and human-robot interaction data of four interactive tasks "hand-shake", "hand-wave", "parachute fist-bump" and "rocket fist-bump". We demonstrate experimentally the importance of predictive and adaptive components as well as low-level abstractions to successfully learn to imitate human behavior in interactive social tasks.
