$π_0$: A Vision-Language-Action Flow Model for General Robot Control
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908753683546112 |
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| author | Black, Kevin Brown, Noah Driess, Danny Esmail, Adnan Equi, Michael Finn, Chelsea Fusai, Niccolo Groom, Lachy Hausman, Karol Ichter, Brian Jakubczak, Szymon Jones, Tim Ke, Liyiming Levine, Sergey Li-Bell, Adrian Mothukuri, Mohith Nair, Suraj Pertsch, Karl Shi, Lucy Xiaoyang Tanner, James Vuong, Quan Walling, Anna Wang, Haohuan Zhilinsky, Ury |
| author_facet | Black, Kevin Brown, Noah Driess, Danny Esmail, Adnan Equi, Michael Finn, Chelsea Fusai, Niccolo Groom, Lachy Hausman, Karol Ichter, Brian Jakubczak, Szymon Jones, Tim Ke, Liyiming Levine, Sergey Li-Bell, Adrian Mothukuri, Mohith Nair, Suraj Pertsch, Karl Shi, Lucy Xiaoyang Tanner, James Vuong, Quan Walling, Anna Wang, Haohuan Zhilinsky, Ury |
| contents | Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_24164 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | $π_0$: A Vision-Language-Action Flow Model for General Robot Control Black, Kevin Brown, Noah Driess, Danny Esmail, Adnan Equi, Michael Finn, Chelsea Fusai, Niccolo Groom, Lachy Hausman, Karol Ichter, Brian Jakubczak, Szymon Jones, Tim Ke, Liyiming Levine, Sergey Li-Bell, Adrian Mothukuri, Mohith Nair, Suraj Pertsch, Karl Shi, Lucy Xiaoyang Tanner, James Vuong, Quan Walling, Anna Wang, Haohuan Zhilinsky, Ury Machine Learning Robotics Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes. |
| title | $π_0$: A Vision-Language-Action Flow Model for General Robot Control |
| topic | Machine Learning Robotics |
| url | https://arxiv.org/abs/2410.24164 |