  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.
