arXiv:2604.20348v2 Announce Type: replace-cross 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 Le
The rapid advancement and growing capabilities of Large Language Models (LLMs) are enabling their application to increasingly complex real-world robotic control problems.
This development indicates a significant step towards more autonomous and capable robotic systems, particularly in complex manipulation tasks, potentially expanding automation beyond structured environments.
The ability to use LLMs and In-Context Learning for bimanual manipulation, especially with high-dimensional action spaces, makes robotic tasks requiring fine motor coordination more feasible without extensive task-specific training.
- · Robotics companies
- · Automation industries
- · Logistics and manufacturing
- · AI research institutions
- · Low-skilled manual labor
- · Companies reliant on traditional, rigid automation
Increased research and development into LLM-driven robotics for complex tasks.
Accelerated adoption of advanced bimanual robotic systems in various industrial and service sectors.
Shift in human-robot collaboration paradigms, with robots handling more intricate physical tasks autonomously.
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Read at arXiv cs.AI