低剛性ロボットの経時的変化を考慮した視覚サーボの自己教師あり学習
Self-Supervised Learning of Visual Servoing for Low-Rigidity Robots Considering Temporal Body Changes
低剛性ロボットが自身の身体変化に適応しながら物体把持のための視覚サーボを自己教師あり学習で獲得する手法を開発し、6軸アームで有効性を確認した。
著者: Kento Kawaharazuka, Naoaki Kanazawa, Kei Okada, Masayuki Inaba
分類: cs.RO
原文アブストラクト
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.
