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ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation

Authors :
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.
Publication Year :
2024

Abstract

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.<br />Comment: Project website: aloha-2.github.io

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2405.02292
Document Type :
Working Paper