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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.03294 |
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| _version_ | 1866912486679117824 |
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| author | Gao, George Jiayuan Li, Tianyu Figueroa, Nadia |
| author_facet | Gao, George Jiayuan Li, Tianyu Figueroa, Nadia |
| contents | We propose an object-centric recovery (OCR) framework to address the challenges of out-of-distribution (OOD) scenarios in visuomotor policy learning. Previous behavior cloning (BC) methods rely heavily on a large amount of labeled data coverage, failing in unfamiliar spatial states. Without relying on extra data collection, our approach learns a recovery policy constructed by an inverse policy inferred from the object keypoint manifold gradient in the original training data. The recovery policy serves as a simple add-on to any base visuomotor BC policy, agnostic to a specific method, guiding the system back towards the training distribution to ensure task success even in OOD situations. We demonstrate the effectiveness of our object-centric framework in both simulation and real robot experiments, achieving an improvement of 77.7\% over the base policy in OOD. Furthermore, we show OCR's capacity to autonomously collect demonstrations for continual learning. Overall, we believe this framework represents a step toward improving the robustness of visuomotor policies in real-world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning Gao, George Jiayuan Li, Tianyu Figueroa, Nadia Robotics Artificial Intelligence We propose an object-centric recovery (OCR) framework to address the challenges of out-of-distribution (OOD) scenarios in visuomotor policy learning. Previous behavior cloning (BC) methods rely heavily on a large amount of labeled data coverage, failing in unfamiliar spatial states. Without relying on extra data collection, our approach learns a recovery policy constructed by an inverse policy inferred from the object keypoint manifold gradient in the original training data. The recovery policy serves as a simple add-on to any base visuomotor BC policy, agnostic to a specific method, guiding the system back towards the training distribution to ensure task success even in OOD situations. We demonstrate the effectiveness of our object-centric framework in both simulation and real robot experiments, achieving an improvement of 77.7\% over the base policy in OOD. Furthermore, we show OCR's capacity to autonomously collect demonstrations for continual learning. Overall, we believe this framework represents a step toward improving the robustness of visuomotor policies in real-world settings. |
| title | Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2411.03294 |