Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866912495249129472 |
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| author | Chen, Annie S. Chada, Govind Smith, Laura Sharma, Archit Fu, Zipeng Levine, Sergey Finn, Chelsea |
| author_facet | Chen, Annie S. Chada, Govind Smith, Laura Sharma, Archit Fu, Zipeng Levine, Sergey Finn, Chelsea |
| contents | To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios during deployment, by drawing upon a diverse repertoire of previouslylearned behaviors. Our approach, RObust Autonomous Modulation (ROAM), introduces a mechanism based on the perceived value of pre-trained behaviors to select and adapt pre-trained behaviors to the situation at hand. Crucially, this adaptation process all happens within a single episode at test time, without any human supervision. We demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped, even successfully moving forward with roller skates on its feet. Our approach adapts over 2x as efficiently compared to existing methods when facing a variety of out-of-distribution situations during deployment by effectively choosing and adapting relevant behaviors on-the-fly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_01059 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment Chen, Annie S. Chada, Govind Smith, Laura Sharma, Archit Fu, Zipeng Levine, Sergey Finn, Chelsea Robotics Machine Learning To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios during deployment, by drawing upon a diverse repertoire of previouslylearned behaviors. Our approach, RObust Autonomous Modulation (ROAM), introduces a mechanism based on the perceived value of pre-trained behaviors to select and adapt pre-trained behaviors to the situation at hand. Crucially, this adaptation process all happens within a single episode at test time, without any human supervision. We demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped, even successfully moving forward with roller skates on its feet. Our approach adapts over 2x as efficiently compared to existing methods when facing a variety of out-of-distribution situations during deployment by effectively choosing and adapting relevant behaviors on-the-fly. |
| title | Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2311.01059 |