人間のデモンストレーションからの視覚ベースロボットマニピュレーション学習
Vision-based Robot Manipulation Learning via Human Demonstrations
三人称視点の単一デモ動画から行動認識と物体検出を用いてマニピュレーションスキルを学習し、テキスト知識ベースで対象物体を推論することで未知の物体や環境への汎化を実現した。
著者: Zhixin Jia, Mengxiang Lin, Zhixin Chen, Shibo Jian
分類: cs.RO
原文アブストラクト
Vision-based learning methods provide promise for robots to learn complex manipulation tasks. However, how to generalize the learned manipulation skills to real-world interactions remains an open question. In this work, we study robotic manipulation skill learning from a single third-person view demonstration by using activity recognition and object detection in computer vision. To facilitate generalization across objects and environments, we propose to use a prior knowledge base in the form of a text corpus to infer the object to be interacted with in the context of a robot. We evaluate our approach in a real-world robot, using several simple and complex manipulation tasks commonly performed in daily life. The experimental results show that our approach achieves good generalization performance even from small amounts of training data.
