🤖 AI Summary
Robots frequently fail to execute everyday tasks due to natural language instructions lacking commonsense preconditions and decomposed subgoals. This paper proposes an LLM-augmented symbolic planning framework that, for the first time, automatically formalizes implicit preconditions and refined subgoals generated by large language models (LLMs) and integrates them into classical planners (e.g., PDDL), enabling end-to-end instruction-to-executable-plan completion. The method unifies natural language understanding, formal modeling, and robot simulation, and is validated in dynamic environments. Experiments demonstrate significant improvements over baseline planners in both valid plan generation rate and task success rate, alongside enhanced environmental adaptability and robustness. The core contribution lies in establishing a verifiable and interpretable synergy between LLM-based commonsense reasoning and symbolic planning—bridging neural and symbolic AI in a principled, transparent manner.
📝 Abstract
Robots often fail at everyday tasks because instructions skip commonsense details like hidden preconditions and small subgoals. Traditional symbolic planners need these details to be written explicitly, which is time consuming and often incomplete. In this project we combine a Large Language Model with symbolic planning. Given a natural language task, the LLM suggests plausible preconditions and subgoals. We translate these suggestions into a formal planning model and execute the resulting plan in simulation. Compared to a baseline planner without the LLM step, our system produces more valid plans, achieves a higher task success rate, and adapts better when the environment changes. These results suggest that adding LLM commonsense to classical planning can make robot behavior in realistic scenarios more reliable.