  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.
