Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916739148677120 |
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| author | Wilcox, Albert Ghanem, Mohamed Moghani, Masoud Barroso, Pierre Joffe, Benjamin Garg, Animesh |
| author_facet | Wilcox, Albert Ghanem, Mohamed Moghani, Masoud Barroso, Pierre Joffe, Benjamin Garg, Animesh |
| contents | Imitation Learning can train robots to perform complex and diverse manipulation tasks, but learned policies are brittle with observations outside of the training distribution. 3D scene representations that incorporate observations from calibrated RGBD cameras have been proposed as a way to mitigate this, but in our evaluations with unseen embodiments and camera viewpoints they show only modest improvement. To address those challenges, we propose Adapt3R, a general-purpose 3D observation encoder which synthesizes data from calibrated RGBD cameras into a vector that can be used as conditioning for arbitrary IL algorithms. The key idea is to use a pretrained 2D backbone to extract semantic information, using 3D only as a medium to localize this information with respect to the end-effector. We show across 93 simulated and 6 real tasks that when trained end-to-end with a variety of IL algorithms, Adapt3R maintains these algorithms' learning capacity while enabling zero-shot transfer to novel embodiments and camera poses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04877 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning Wilcox, Albert Ghanem, Mohamed Moghani, Masoud Barroso, Pierre Joffe, Benjamin Garg, Animesh Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics Imitation Learning can train robots to perform complex and diverse manipulation tasks, but learned policies are brittle with observations outside of the training distribution. 3D scene representations that incorporate observations from calibrated RGBD cameras have been proposed as a way to mitigate this, but in our evaluations with unseen embodiments and camera viewpoints they show only modest improvement. To address those challenges, we propose Adapt3R, a general-purpose 3D observation encoder which synthesizes data from calibrated RGBD cameras into a vector that can be used as conditioning for arbitrary IL algorithms. The key idea is to use a pretrained 2D backbone to extract semantic information, using 3D only as a medium to localize this information with respect to the end-effector. We show across 93 simulated and 6 real tasks that when trained end-to-end with a variety of IL algorithms, Adapt3R maintains these algorithms' learning capacity while enabling zero-shot transfer to novel embodiments and camera poses. |
| title | Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2503.04877 |