Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks

Isabell Wochner, Pierre Schumacher, Georg Martius, Dieter Büchler, Syn Schmitt, Daniel Haeufle
Proceedings of The 6th Conference on Robot Learning, PMLR 205:1178-1188, 2023.

Abstract

Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favorable to learning. While recent advances have been made in applying modern learning techniques to muscle-actuated systems both in simulation as well as in robotics, so far, no detailed analysis has been performed to show the benefits of muscles in this setting. Our study closes this gap by investigating core robotics challenges and comparing the performance of different actuator morphologies in terms of data-efficiency, hyperparameter sensitivity, and robustness.

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-wochner23a, title = {Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks}, author = {Wochner, Isabell and Schumacher, Pierre and Martius, Georg and B\"uchler, Dieter and Schmitt, Syn and Haeufle, Daniel}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {1178--1188}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/wochner23a/wochner23a.pdf}, url = {https://proceedings.mlr.press/v205/wochner23a.html}, abstract = {Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favorable to learning. While recent advances have been made in applying modern learning techniques to muscle-actuated systems both in simulation as well as in robotics, so far, no detailed analysis has been performed to show the benefits of muscles in this setting. Our study closes this gap by investigating core robotics challenges and comparing the performance of different actuator morphologies in terms of data-efficiency, hyperparameter sensitivity, and robustness.} }
Endnote
%0 Conference Paper %T Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks %A Isabell Wochner %A Pierre Schumacher %A Georg Martius %A Dieter Büchler %A Syn Schmitt %A Daniel Haeufle %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-wochner23a %I PMLR %P 1178--1188 %U https://proceedings.mlr.press/v205/wochner23a.html %V 205 %X Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favorable to learning. While recent advances have been made in applying modern learning techniques to muscle-actuated systems both in simulation as well as in robotics, so far, no detailed analysis has been performed to show the benefits of muscles in this setting. Our study closes this gap by investigating core robotics challenges and comparing the performance of different actuator morphologies in terms of data-efficiency, hyperparameter sensitivity, and robustness.
APA
Wochner, I., Schumacher, P., Martius, G., Büchler, D., Schmitt, S. & Haeufle, D.. (2023). Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:1178-1188 Available from https://proceedings.mlr.press/v205/wochner23a.html.

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