Robotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback

Excerpt

arXiv:2503.21969v4 Announce Type: replace-cross Abstract: Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-conditioned frameworks often rely on dense step-wise feedback, learned action policies, or unstructured prompting, which limits robustness and generalization. We propose DAHLIA, a data-agnostic code-generation framework that treats long-horizon manipulation as episodic task planning and evaluat