Bimanual Robot Manipulation via Multi-Agent In-Context Learning

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. Evaluated on 13 tasks from the TWIN benchmark, BiCICLe achieves 70.5% average success rate, outperforming the best training-free baseline by 6.1 percentage points and surpassing most supervised methods. We also demonstrate superior real-world performance on 3 tasks without hardware-specific retraining.

Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

I.2.9; I.2.11; I.2.6

arXiv:2604.20348 [cs.RO]

(or arXiv:2604.20348v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.20348 Focus to learn more arXiv-issued DOI via DataCite

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