Online Transfer and Adaptation of Tactile Skill: A Teleoperation Framework

Xiao Chen, Tianle Ni, Kübra Karacan, Hamid Sadeghian, Sami Haddadin

Keywords: Learning from Demonstration, Online Adaptation, Tactile Skill, Teleoperation, Autonomy Allocation

TL;DR: A tele-teaching framework enables robot online learning and adapting tactile skill from a remote demonstration.

Abstract: This paper presents a teleoperation framework designed for online learning and adaptation of tactile skills, which provides an intuitive interface without need for physical access to 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 the learning confidence and operator intention, ensures a smooth transition between human demonstration to autonomous robot operation. Our experimental results with two 7 Degree of Freedom (DoF) Franka Panda robot demonstrates that the tele-teaching framework facilitates online motion and force learning and adaptation within a few iterations.

Video: https://www.youtube.com/watch?v=LYnUJ0cYJgs

Student Paper: yes

Submission Number: 396