Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning

2026-03-03

AI Summary

This work addresses the challenge of accurate state estimation in partially observable environments, where robotic task and motion planning is often hindered by task-irrelevant unexpected objects. The authors propose CoCo-TAMP, a novel framework that integrates the commonsense reasoning capabilities of large language models (LLMs) into hierarchical state estimation for the first time. By modeling object location priors and co-occurrence relationships, CoCo-TAMP refines belief states over task-relevant objects without requiring manually constructed knowledge bases. This approach significantly enhances the efficiency of long-horizon task and motion planning. Experimental results demonstrate that CoCo-TAMP reduces planning and execution time by 62.7% on average in simulation and achieves a 72.6% improvement in real-robot trials, substantially outperforming baseline methods lacking commonsense reasoning.

Technology Category

Intelligent Robots: State EstimationHumans and AI: Human-Aware Planning and Behavior PredictionPlanning, Routing, and Scheduling: Planning with Language Models

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Robot planning in partially observable environments, where not all objects are known or visible, is a challenging problem, as it requires reasoning under uncertainty through partially observable Markov decision processes. During the execution of a computed plan, a robot may unexpectedly observe task-irrelevant objects, which are typically ignored by naive planners. In this work, we propose incorporating two types of common-sense knowledge: (1) certain objects are more likely to be found in specific locations; and (2) similar objects are likely to be co-located, while dissimilar objects are less likely to be found together. Manually engineering such knowledge is complex, so we explore leveraging the powerful common-sense reasoning capabilities of large language models (LLMs). Our planning and execution framework, CoCo-TAMP, introduces a hierarchical state estimation that uses LLM-guided information to shape the belief over task-relevant objects, enabling efficient solutions to long-horizon task and motion planning problems. In experiments, CoCo-TAMP achieves an average reduction of 62.7 in planning and execution time in simulation, and 72.6 in real-world demonstrations, compared to a baseline that does not incorporate either type of common-sense knowledge.

Problem

• partially observable environments
• task and motion planning
• state estimation
• common-sense reasoning
• robot planning

Innovation

• Large Language Models
• Common-sense Reasoning
• Partially Observable TAMP
• Hierarchical State Estimation
• Task and Motion Planning