Yu, Wanming;
Yang, Chuanyu;
McGreavy, Christopher;
Triantafyllidis, Eleftherios;
Bellegarda, Guillaume;
Shafiee, Milad;
Ijspeert, Auke Jan;
(2023)
Identifying important sensory feedback for learning locomotion skills.
Nature Machine Intelligence
, 5
(8)
pp. 919-932.
10.1038/s42256-023-00701-w.
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Abstract
Robot motor skills can be acquired by deep reinforcement learning as neural networks to refect state–action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neural networks, human insights and engineering eforts are often required to select appropriate states through qualitative approaches, such as ablation studies, without a quantitative analysis of the state importance. Here we present a systematic saliency analysis that quantitatively evaluates the relative importance of diferent feedback states for motor skills learned through deep reinforcement learning. Our approach provides a guideline to identify the most essential feedback states for robot motor learning. By using only the important states including joint positions, gravity vector and base linear and angular velocities, we demonstrate that a simulated quadruped robot can learn various robust locomotion skills. We fnd that locomotion skills learned only with important states can achieve task performance comparable to the performance of those with more states. This work provides quantitative insights into the impacts of state observations on specifc types of motor skills, enabling the learning of a wide range of motor skills with minimal sensing dependencies.
| Type: | Article |
|---|---|
| Title: | Identifying important sensory feedback for learning locomotion skills |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s42256-023-00701-w |
| Publisher version: | https://doi.org/10.1038/s42256-023-00701-w |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10177099 |
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