触覚サーボのための潜在空間ダイナミクス学習
Learning Latent Space Dynamics for Tactile Servoing
触覚センサ情報を2次元多様体として扱い、デモンストレーションから触覚サーボ用のダイナミクスモデルを学習する手法を提案し、接触点追従タスクで評価した。
著者: Giovanni Sutanto, Nathan Ratliff, Balakumar Sundaralingam, Yevgen Chebotar, Zhe Su, Ankur Handa, Dieter Fox
分類: cs.RO
原文アブストラクト
To achieve a dexterous robotic manipulation, we need to endow our robot with tactile feedback capability, i.e. the ability to drive action based on tactile sensing. In this paper, we specifically address the challenge of tactile servoing, i.e. given the current tactile sensing and a target/goal tactile sensing --memorized from a successful task execution in the past-- what is the action that will bring the current tactile sensing to move closer towards the target tactile sensing at the next time step. We develop a data-driven approach to acquire a dynamics model for tactile servoing by learning from demonstration. Moreover, our method represents the tactile sensing information as to lie on a surface --or a 2D manifold-- and perform a manifold learning, making it applicable to any tactile skin geometry. We evaluate our method on a contact point tracking task using a robot equipped with a tactile finger. A video demonstrating our approach can be seen in https://youtu.be/0QK0-Vx7WkI
