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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/2410.13126 |
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| _version_ | 1866913550432206848 |
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| author | Zhao, Tony Z. Tompson, Jonathan Driess, Danny Florence, Pete Ghasemipour, Kamyar Finn, Chelsea Wahid, Ayzaan |
| author_facet | Zhao, Tony Z. Tompson, Jonathan Driess, Danny Florence, Pete Ghasemipour, Kamyar Finn, Chelsea Wahid, Ayzaan |
| contents | Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simple recipe of large scale data collection on the ALOHA 2 platform, combined with expressive models such as Diffusion Policies, can be effective in learning challenging bimanual manipulation tasks involving deformable objects and complex contact rich dynamics. We demonstrate our recipe on 5 challenging real-world and 3 simulated tasks and demonstrate improved performance over state-of-the-art baselines. The project website and videos can be found at aloha-unleashed.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13126 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ALOHA Unleashed: A Simple Recipe for Robot Dexterity Zhao, Tony Z. Tompson, Jonathan Driess, Danny Florence, Pete Ghasemipour, Kamyar Finn, Chelsea Wahid, Ayzaan Robotics Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simple recipe of large scale data collection on the ALOHA 2 platform, combined with expressive models such as Diffusion Policies, can be effective in learning challenging bimanual manipulation tasks involving deformable objects and complex contact rich dynamics. We demonstrate our recipe on 5 challenging real-world and 3 simulated tasks and demonstrate improved performance over state-of-the-art baselines. The project website and videos can be found at aloha-unleashed.github.io. |
| title | ALOHA Unleashed: A Simple Recipe for Robot Dexterity |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.13126 |