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In this study, we develop a simple daily assistive robot that controls itsown vision according to linguistic instructions. The robot performs severaldaily tasks such as recording a user's face, hands, or screen, and remotelycapturing images of desired locations. To construct such a robot, we combine apre-trained large-scale vision-language model with a low-cost low-rigidityrobot arm. The correlation between the robot's physical and visual informationis learned probabilistically using a neural network, and changes in theprobability distribution based on changes in time and environment areconsidered by parametric bias, which is a learnable network input variable. Wedemonstrate the effectiveness of this learning method by open-vocabulary viewcontrol experiments with an actual robot arm, MyCobot.

Daily Assistive View Control Learning of Low-Cost Low-Rigidity Robot via Large-Scale Vision-Language Model