大規模視覚言語モデルを用いた低コスト低剛性ロボットの日常支援視野制御学習
Daily Assistive View Control Learning of Low-Cost Low-Rigidity Robot via Large-Scale Vision-Language Model
大規模視覚言語モデルと低コスト・低剛性ロボットアームを組み合わせ、言語指示に従ってロボットが自身の視野を制御する手法を開発した。実機実験でオープンボキャブラリな視野制御の有効性を示した。
著者: Kento Kawaharazuka, Naoaki Kanazawa, Yoshiki Obinata, Kei Okada, Masayuki Inaba
分類: cs.RO
原文アブストラクト
In this study, we develop a simple daily assistive robot that controls its own vision according to linguistic instructions. The robot performs several daily tasks such as recording a user's face, hands, or screen, and remotely capturing images of desired locations. To construct such a robot, we combine a pre-trained large-scale vision-language model with a low-cost low-rigidity robot arm. The correlation between the robot's physical and visual information is learned probabilistically using a neural network, and changes in the probability distribution based on changes in time and environment are considered by parametric bias, which is a learnable network input variable. We demonstrate the effectiveness of this learning method by open-vocabulary view control experiments with an actual robot arm, MyCobot.
