Self-Supervised Learning of Visual Servoing for Low-Rigidity Robots Considering Temporal Body Changes

TL;DR

This paper presents a self-supervised learning method enabling low-rigidity robots to adaptively perform visual servoing despite body changes over time, demonstrated on a 6-axis MyCobot arm for object grasping.

Contribution

It introduces a novel autonomous learning approach for visual servoing in low-rigidity robots that accounts for temporal body changes.

Findings

Effective grasping demonstrated on MyCobot robot.

Adaptive visual servoing improves robustness to body changes.

Method reduces calibration time and enhances autonomy.

Abstract

In this study, we investigate object grasping by visual servoing in a low-rigidity robot. It is difficult for a low-rigidity robot to handle its own body as intended compared to a rigid robot, and calibration between vision and body takes some time. In addition, the robot must constantly adapt to changes in its body, such as the change in camera position and change in joints due to aging. Therefore, we develop a method for a low-rigidity robot to autonomously learn visual servoing of its body. We also develop a mechanism that can adaptively change its visual servoing according to temporal body changes. We apply our method to a low-rigidity 6-axis arm, MyCobot, and confirm its effectiveness by conducting object grasping experiments based on visual servoing.