Bimanual Robot Manipulation via Multi-Agent In-Context Learning

• URL: http://arxiv.org/abs/2604.20348v1
• Date: Wed, 22 Apr 2026 08:51:07 GMT
• Title: Bimanual Robot Manipulation via Multi-Agent In-Context Learning
• Abstract summary: BiCICLe is first framework that enables standard LLMs to perform few-shot bimanual manipulation without fine-tuning.<n> evaluated on 13 tasks from the TWIN benchmark, BiCICLe achieves up to 71.1% average success rate.
• Score: 19.589504654145326
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf, text-only LLMs to predict robot actions without any task-specific training while preserving their generalization capabilities. Applying ICL to bimanual manipulation remains challenging, as the high-dimensional joint action space and tight inter-arm coordination constraints rapidly overwhelm standard context windows. To address this, we introduce BiCICLe (Bimanual Coordinated In-Context Learning), the first framework that enables standard LLMs to perform few-shot bimanual manipulation without fine-tuning. BiCICLe frames bimanual control as a multi-agent leader-follower problem, decoupling the action space into sequential, conditioned single-arm predictions. This naturally extends to Arms' Debate, an iterative refinement process, and to the introduction of a third LLM-as-Judge to evaluate and select the most plausible coordinated trajectories. Evaluated on 13 tasks from the TWIN benchmark, BiCICLe achieves up to 71.1% average success rate, outperforming the best training-free baseline by 6.7 percentage points and surpassing most supervised methods. We further demonstrate strong few-shot generalization on novel tasks.

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