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.

Hoku Labs, Authors listed in alphabetical order, with contributions listed in Appendix. 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. Bottom: A detailed image of an ALOHA 2 workcell with gravity compensation, the redesigned leader and follower grippers, and images from the frame-mounted cameras. ALOHA 2, like the original Zhao et al. (2023), consists of a bimanual parallel-jaw gripper workcell with two ViperX 6-DoF arms (Trossen Robotics, a) (the "follower"), along with 2 smaller WidowX arms (Trossen Robotics, b) (the "leader").