November 9, 2025WileyOpen Access

Scalable Task Planning via Large Language Models and Structured World Representations

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This work demonstrates how applying commonsense knowledge can reduce complexity in task planning, validating results on a 7-DoF manipulator.

State space was effectively pruned to manage complexity in planning tasks, enhancing efficiency with minimal resource use.
Key evidence supports reduced computational load while ensuring comprehensive task execution across environments.
Observational analysis across various simulations illustrated the practical applications of large language models in real-world settings.
Results indicate significant improvements in operational efficiency, promoting wider use of advanced planning methods.

Pérez‐Dattari et al. (2025) studied this question.

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November 9, 2025WileyOpen Access

View Full Paper

Ask AI

Bookmark

Authors

This work demonstrates how applying commonsense knowledge can reduce complexity in task planning, validating results on a 7-DoF manipulator.

State space was effectively pruned to manage complexity in planning tasks, enhancing efficiency with minimal resource use.
Key evidence supports reduced computational load while ensuring comprehensive task execution across environments.
Observational analysis across various simulations illustrated the practical applications of large language models in real-world settings.
Results indicate significant improvements in operational efficiency, promoting wider use of advanced planning methods.

Pérez‐Dattari et al. (2025) studied this question.

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