Learning-based legged locomotion; state of the art and future perspectives
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910709638496256 |
|---|---|
| author | Ha, Sehoon Lee, Joonho van de Panne, Michiel Xie, Zhaoming Yu, Wenhao Khadiv, Majid |
| author_facet | Ha, Sehoon Lee, Joonho van de Panne, Michiel Xie, Zhaoming Yu, Wenhao Khadiv, Majid |
| contents | Legged locomotion holds the premise of universal mobility, a critical capability for many real-world robotic applications. Both model-based and learning-based approaches have advanced the field of legged locomotion in the past three decades. In recent years, however, a number of factors have dramatically accelerated progress in learning-based methods, including the rise of deep learning, rapid progress in simulating robotic systems, and the availability of high-performance and affordable hardware. This article aims to give a brief history of the field, to summarize recent efforts in learning locomotion skills for quadrupeds, and to provide researchers new to the area with an understanding of the key issues involved. With the recent proliferation of humanoid robots, we further outline the rapid rise of analogous methods for bipedal locomotion. We conclude with a discussion of open problems as well as related societal impact. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01152 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-based legged locomotion; state of the art and future perspectives Ha, Sehoon Lee, Joonho van de Panne, Michiel Xie, Zhaoming Yu, Wenhao Khadiv, Majid Robotics Legged locomotion holds the premise of universal mobility, a critical capability for many real-world robotic applications. Both model-based and learning-based approaches have advanced the field of legged locomotion in the past three decades. In recent years, however, a number of factors have dramatically accelerated progress in learning-based methods, including the rise of deep learning, rapid progress in simulating robotic systems, and the availability of high-performance and affordable hardware. This article aims to give a brief history of the field, to summarize recent efforts in learning locomotion skills for quadrupeds, and to provide researchers new to the area with an understanding of the key issues involved. With the recent proliferation of humanoid robots, we further outline the rapid rise of analogous methods for bipedal locomotion. We conclude with a discussion of open problems as well as related societal impact. |
| title | Learning-based legged locomotion; state of the art and future perspectives |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.01152 |