GPT-PDDL: Towards Executable Robot Task Planning

Abstract

Given the recent significant advancements in the video understanding capabilities of Large Language Models (LLMs), there is growing interest in research that automatically generates executable robot task plans from human demonstration videos. Existing LLM-based symbolic planning approaches often rely on manually defined Problem Domain Definition Language (PDDL) domains or fixed action primitives. This paper proposes GPT-PDDL, a framework that infers step-by-step task procedures from demonstration videos and converts them into robot plans based on PDDL.