Chain-of-Thought Planning

Using large language models' chain-of-thought prompting to generate step-by-step task plans for robots. The LLM reasons through a task (e.g., 'to make coffee: 1. Find mug 2. Fill with water 3. Heat...') and each step is grounded to executable robot actions via skill primitives or VLAs. CoT planning improves long-horizon task success rates.