This research introduces a novel approach to robot control using embodied chain-of-thought reasoning, enabling robots to think through complex tasks step-by-step.

• Combines large vision-language models with iterative reasoning for improved robot decision-making
• Significantly enhances robustness and generalization beyond training data
• Enables robots to break down complex tasks into manageable steps
• Represents a key advancement in making robots more adaptable to real-world scenarios

For engineering teams, this approach offers a promising path to developing more reliable robotic systems that can handle novel situations and environments without extensive retraining.

Robotic Control via Embodied Chain-of-Thought Reasoning