ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation
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.
To support research on complex manipulation tasks, we aim to significantly scale up data collection on the ALOHA platform, including the number of robots in use, the amount of hours of data per robot, and the diversity of data collection. This scaling process shifts the requirements and scope relative to the first ALOHA platform. For ALOHA 2, we build on the strengths of the ALOHA platform while also targeting the following areas for further improvement:
For smoother teleoperation and improved ergonomics, we replace the original scissor leader gripper design from ALOHA with a low friction rail design with reduced mechanical complexity.
