Robotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback
The work proposes DAHLIA, a closed-loop framework for long-horizon robotic manipulation that uses Large Language Models (LLMs) to generate executable code instead of relying on pre-trained controllers. It combines a planner tunnel using Chain-of-Thought prompting with in-context learning and a reporter tunnel powered by Vision-Language Models to provide structured feedback, allowing the system to recover from errors and generalize well across complex tasks.