Equivariant Diffusion Policy
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913546534649856 |
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| author | Wang, Dian Hart, Stephen Surovik, David Kelestemur, Tarik Huang, Haojie Zhao, Haibo Yeatman, Mark Wang, Jiuguang Walters, Robin Platt, Robert |
| author_facet | Wang, Dian Hart, Stephen Surovik, David Kelestemur, Tarik Huang, Haojie Zhao, Haibo Yeatman, Mark Wang, Jiuguang Walters, Robin Platt, Robert |
| contents | Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning. However, a drawback of this approach is the need to learn a denoising function, which is significantly more complex than learning an explicit policy. In this work, we propose Equivariant Diffusion Policy, a novel diffusion policy learning method that leverages domain symmetries to obtain better sample efficiency and generalization in the denoising function. We theoretically analyze the $\mathrm{SO}(2)$ symmetry of full 6-DoF control and characterize when a diffusion model is $\mathrm{SO}(2)$-equivariant. We furthermore evaluate the method empirically on a set of 12 simulation tasks in MimicGen, and show that it obtains a success rate that is, on average, 21.9% higher than the baseline Diffusion Policy. We also evaluate the method on a real-world system to show that effective policies can be learned with relatively few training samples, whereas the baseline Diffusion Policy cannot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Equivariant Diffusion Policy Wang, Dian Hart, Stephen Surovik, David Kelestemur, Tarik Huang, Haojie Zhao, Haibo Yeatman, Mark Wang, Jiuguang Walters, Robin Platt, Robert Robotics Machine Learning Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning. However, a drawback of this approach is the need to learn a denoising function, which is significantly more complex than learning an explicit policy. In this work, we propose Equivariant Diffusion Policy, a novel diffusion policy learning method that leverages domain symmetries to obtain better sample efficiency and generalization in the denoising function. We theoretically analyze the $\mathrm{SO}(2)$ symmetry of full 6-DoF control and characterize when a diffusion model is $\mathrm{SO}(2)$-equivariant. We furthermore evaluate the method empirically on a set of 12 simulation tasks in MimicGen, and show that it obtains a success rate that is, on average, 21.9% higher than the baseline Diffusion Policy. We also evaluate the method on a real-world system to show that effective policies can be learned with relatively few training samples, whereas the baseline Diffusion Policy cannot. |
| title | Equivariant Diffusion Policy |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2407.01812 |