Enhancing Cognitive Robotics with Commonsense through LLM-Generated Preconditions and Subgoals

• URL: http://arxiv.org/abs/2512.00069v1
• Date: Mon, 24 Nov 2025 11:16:41 GMT
• Title: Enhancing Cognitive Robotics with Commonsense through LLM-Generated Preconditions and Subgoals
• Authors: Ohad Bachner, Bar Gamliel,
• Abstract summary: Traditional symbolic planners need hidden preconditions and small subgoals to be written explicitly.<n>In this project we combine a Large Language Model with symbolic planning.<n>Our system produces more valid plans, achieves a higher task success rate, and adapts better when the environment changes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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