Peng, Tianhu;
Bao, Lingfan;
Humphreys, Joseph;
Delfaki, Andromachi Maria;
Kanoulas, Dimitrios;
Zhou, Chengxu;
(2024)
Learning Bipedal Walking on a Quadruped Robot via Adversarial Motion Priors.
In: Huda, M Nazmul and Wang, Mingfeng and Kalganova, Tatiana, (eds.)
Towards Autonomous Robotic Systems:TAROS 2024.
(pp. pp. 118-129).
Springer: Cham, Switzerland.
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Abstract
Previous studies have successfully demonstrated agile and robust locomotion in challenging terrains for quadrupedal robots. However, the bipedal locomotion mode for quadruped robots remains unverified. This paper explores the adaptation of a learning framework originally designed for quadrupedal robots to operate blind locomotion in biped mode. We leverage a framework that incorporates Adversarial Motion Priors with a teacher-student policy to enable imitation of a reference trajectory and navigation on tough terrain. Our work involves transferring and evaluating a similar learning framework on a quadruped robot in biped mode, aiming to achieve stable walking on both flat and complicated terrains. Our simulation results demonstrate that the trained policy enables the quadruped robot to navigate both flat and challenging terrains, including stairs and uneven surfaces.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning Bipedal Walking on a Quadruped Robot via Adversarial Motion Priors |
| Event: | 25th Annual Conference, TAROS 2024 |
| ISBN-13: | 978-3-031-72061-1 |
| DOI: | 10.1007/978-3-031-72062-8_11 |
| Publisher version: | https://doi.org/10.1007/978-3-031-72062-8_11 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Legged Robots; Bipedal Locomotion; Deep Reinforcement Learning; Adversarial Motion Priors |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10204233 |
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