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

Abstract

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.

accepted at Humanoids2023

References in corpus (3)

• Vision-Language Intelligence: Tasks, Representation Learning, and Large Models
• VQA-based Robotic State Recognition Optimized with Genetic Algorithm
• Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots