Bimanual Robot Manipulation via Multi-Agent In-Context Learning
Large 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 contro...
| Authors | Alessio Palma, Indro Spinelli, Vignesh Prasad et al. |
|---|---|
| Primary category | cs.RO |
| Published | 22 April 2026 |
| Last updated at source | 29 September 2026 |
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