BiRoDiff: Diffusion policies for bipedal robot locomotion on unseen terrains
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916315662385152 |
|---|---|
| author | Mothish, GVS Tayal, Manan Kolathaya, Shishir |
| author_facet | Mothish, GVS Tayal, Manan Kolathaya, Shishir |
| contents | Locomotion on unknown terrains is essential for bipedal robots to handle novel real-world challenges, thus expanding their utility in disaster response and exploration. In this work, we introduce a lightweight framework that learns a single walking controller that yields locomotion on multiple terrains. We have designed a real-time robot controller based on diffusion models, which not only captures multiple behaviours with different velocities in a single policy but also generalizes well for unseen terrains. Our controller learns with offline data, which is better than online learning in aspects like scalability, simplicity in training scheme etc. We have designed and implemented a diffusion model-based policy controller in simulation on our custom-made Bipedal Robot model named Stoch BiRo. We have demonstrated its generalization capability and high frequency control step generation relative to typical generative models, which require huge onboarding compute. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05424 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BiRoDiff: Diffusion policies for bipedal robot locomotion on unseen terrains Mothish, GVS Tayal, Manan Kolathaya, Shishir Robotics Artificial Intelligence Systems and Control Locomotion on unknown terrains is essential for bipedal robots to handle novel real-world challenges, thus expanding their utility in disaster response and exploration. In this work, we introduce a lightweight framework that learns a single walking controller that yields locomotion on multiple terrains. We have designed a real-time robot controller based on diffusion models, which not only captures multiple behaviours with different velocities in a single policy but also generalizes well for unseen terrains. Our controller learns with offline data, which is better than online learning in aspects like scalability, simplicity in training scheme etc. We have designed and implemented a diffusion model-based policy controller in simulation on our custom-made Bipedal Robot model named Stoch BiRo. We have demonstrated its generalization capability and high frequency control step generation relative to typical generative models, which require huge onboarding compute. |
| title | BiRoDiff: Diffusion policies for bipedal robot locomotion on unseen terrains |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2407.05424 |