Bimanual Robot Manipulation via Multi-Agent In-Context Learning
BiCICLe enables standard LLMs to perform complex bimanual manipulation without fine-tuning by framing it as a leader-follower problem. The Leader predicts its trajectory first from single-arm demonstrations, and the Follower predicts its actions conditioned on that plan. This structured prompting allows LLMs to learn precise inter-arm coordination directly from examples.