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CrossFormers introduce a transformer-based policy that can control a wide variety of robots across different tasks, including manipulation,

The Short Version Read 'Octo: An Open-Source Generalist Robot Policy' from UC Berkeley, Stanford, Carnegie Mellon, and Google DeepMind. Shift from task-specific learning to a flexible, generalist approach for training robots. Train on the diverse Open X-Embodiment dataset with its over 800,000 robot trajectories. Use Octo's transformer-based model to handle multiple robots, tasks, and environments without extensive retraining. Apply 'readout tokens' and 'action chunking' to p

Discover how variations in data collection impact imitation learning with the Aloha Kit. Explore experiments and access datasets on Hugging

Bi-ACT builds upon ACT with an approach that incorporates both position and force information.

Imitation learning is essential in robotics for its ability to quickly and safely teach robots complex tasks.