Online Transfer and Adaptation of Tactile Skill: A Teleoperation Framework

Xiao Chen, Tianle Ni, Kübra Karacan, Hamid Sadeghian, Sami Haddadin
Proceedings of The 8th Conference on Robot Learning, PMLR 270:4981-4995, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v270-chen25i, title = {Online Transfer and Adaptation of Tactile Skill: A Teleoperation Framework}, author = {Chen, Xiao and Ni, Tianle and Karacan, K{\"{u}}bra and Sadeghian, Hamid and Haddadin, Sami}, booktitle = {Proceedings of The 8th Conference on Robot Learning}, pages = {4981--4995}, year = {2025}, editor = {Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram}, volume = {270}, series = {Proceedings of Machine Learning Research}, month = {06--09 Nov}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v270/main/assets/chen25i/chen25i.pdf}, url = {https://proceedings.mlr.press/v270/chen25i.html}, 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.} }
Endnote
%0 Conference Paper %T Online Transfer and Adaptation of Tactile Skill: A Teleoperation Framework %A Xiao Chen %A Tianle Ni %A Kübra Karacan %A Hamid Sadeghian %A Sami Haddadin %B Proceedings of The 8th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Pulkit Agrawal %E Oliver Kroemer %E Wolfram Burgard %F pmlr-v270-chen25i %I PMLR %P 4981--4995 %U https://proceedings.mlr.press/v270/chen25i.html %V 270 %X 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.
APA
Chen, X., Ni, T., Karacan, K., Sadeghian, H. & Haddadin, S.. (2025). Online Transfer and Adaptation of Tactile Skill: A Teleoperation Framework. Proceedings of The 8th Conference on Robot Learning, in Proceedings of Machine Learning Research 270:4981-4995 Available from https://proceedings.mlr.press/v270/chen25i.html.

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