Learning Multi-Skill Legged Locomotion Using Conditional Adversarial Motion Priors
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908560435183616 |
|---|---|
| author | Huang, Ning Xie, Zhentao Li, Qinchuan |
| author_facet | Huang, Ning Xie, Zhentao Li, Qinchuan |
| contents | Despite growing interest in developing legged robots that emulate biological locomotion for agile navigation of complex environments, acquiring a diverse repertoire of skills remains a fundamental challenge in robotics. Existing methods can learn motion behaviors from expert data, but they often fail to acquire multiple locomotion skills through a single policy and lack smooth skill transitions. We propose a multi-skill learning framework based on Conditional Adversarial Motion Priors (CAMP), with the aim of enabling quadruped robots to efficiently acquire a diverse set of locomotion skills from expert demonstrations. Precise skill reconstruction is achieved through a novel skill discriminator and skill-conditioned reward design. The overall framework supports the active control and reuse of multiple skills, providing a practical solution for learning generalizable policies in complex environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Multi-Skill Legged Locomotion Using Conditional Adversarial Motion Priors Huang, Ning Xie, Zhentao Li, Qinchuan Robotics Despite growing interest in developing legged robots that emulate biological locomotion for agile navigation of complex environments, acquiring a diverse repertoire of skills remains a fundamental challenge in robotics. Existing methods can learn motion behaviors from expert data, but they often fail to acquire multiple locomotion skills through a single policy and lack smooth skill transitions. We propose a multi-skill learning framework based on Conditional Adversarial Motion Priors (CAMP), with the aim of enabling quadruped robots to efficiently acquire a diverse set of locomotion skills from expert demonstrations. Precise skill reconstruction is achieved through a novel skill discriminator and skill-conditioned reward design. The overall framework supports the active control and reuse of multiple skills, providing a practical solution for learning generalizable policies in complex environments. |
| title | Learning Multi-Skill Legged Locomotion Using Conditional Adversarial Motion Priors |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.21810 |