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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.02292 |
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| _version_ | 1866910434816163840 |
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| author | ALOHA 2 Team Aldaco, Jorge Armstrong, Travis Baruch, Robert Bingham, Jeff Chan, Sanky Draper, Kenneth Dwibedi, Debidatta Finn, Chelsea Florence, Pete Goodrich, Spencer Gramlich, Wayne Hage, Torr Herzog, Alexander Hoech, Jonathan Nguyen, Thinh Storz, Ian Tabanpour, Baruch Takayama, Leila Tompson, Jonathan Wahid, Ayzaan Wahrburg, Ted Xu, Sichun Yaroshenko, Sergey Zakka, Kevin Zhao, Tony Z. |
| author_facet | ALOHA 2 Team Aldaco, Jorge Armstrong, Travis Baruch, Robert Bingham, Jeff Chan, Sanky Draper, Kenneth Dwibedi, Debidatta Finn, Chelsea Florence, Pete Goodrich, Spencer Gramlich, Wayne Hage, Torr Herzog, Alexander Hoech, Jonathan Nguyen, Thinh Storz, Ian Tabanpour, Baruch Takayama, Leila Tompson, Jonathan Wahid, Ayzaan Wahrburg, Ted Xu, Sichun Yaroshenko, Sergey Zakka, Kevin Zhao, Tony Z. |
| contents | Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design. To accelerate research in large-scale bimanual manipulation, we open source all hardware designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of ALOHA 2 with system identification. See the project website at aloha-2.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02292 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation ALOHA 2 Team Aldaco, Jorge Armstrong, Travis Baruch, Robert Bingham, Jeff Chan, Sanky Draper, Kenneth Dwibedi, Debidatta Finn, Chelsea Florence, Pete Goodrich, Spencer Gramlich, Wayne Hage, Torr Herzog, Alexander Hoech, Jonathan Nguyen, Thinh Storz, Ian Tabanpour, Baruch Takayama, Leila Tompson, Jonathan Wahid, Ayzaan Wahrburg, Ted Xu, Sichun Yaroshenko, Sergey Zakka, Kevin Zhao, Tony Z. Robotics Machine Learning Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design. To accelerate research in large-scale bimanual manipulation, we open source all hardware designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of ALOHA 2 with system identification. See the project website at aloha-2.github.io. |
| title | ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2405.02292 |