Online Transfer and Adaptation of Tactile Skill: A Teleoperation Framework

This paper presents a teleoperation framework designed for online learning and adaptation of tactile skills, which provides an intuitive interface without the need for physical access to an execution robot. The proposed tele-teaching approach utilizes periodical Dynamical Movement Primitives (DMP) and Recursive Least Square (RLS) for generating tactile skills. An autonomy allocation strategy, guided by learning confidence and operator intention, ensures a smooth transition from human demonstration to autonomous robot operation. Our experimental results with two 7 Degree of Freedom (DoF) Franka Panda robots demonstrate that the tele-teaching framework facilitates online motion and force learning and adaptation within a few iterations.