Robot Manipulation Planning with Large Pre-Trained Models of Language, Vision, and Action

This paper explores the integration of large pre-trained models of language, vision, and action to enhance robot manipulation planning. By leveraging advanced language models such as GPT-4 and Gemini, task planning can be articulated in natural language, allowing for intuitive and precise task specifications. The paper describes task and motion planning (TAMP), which is crucial for robot operation, optimizing precise execution by considering the work environment and linking high-level decision-making with detailed motion control.