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November 10, 2025Advanced Robotics ResearchOpen Access

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

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Authors

VBVineet BhatAKAli Umut KaypakRobotics Research (United States)PKPrashanth Krishnamurthy

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Implication

Analysis shows feedback enhances robustness in robotic task planning, boosting success rates in varied environments.

Key Points

  • Task success rates improved using feedback mechanisms and robust algorithms for large language models in robot planning.
  • Feedback from environmental interactions enhanced the robustness of task execution in robotic systems.
  • Observational analysis across complex tasks showed improvements in task-oriented success by 17% in simulation and real-world environments.
  • Robustness of robot task planning suggests potential for enhanced reliability in practical applications.

Cite This Study

Bhat et al. (2025) studied this question.

synapsesocial.com/papers/69253a31c0ce034ddc357839https://doi.org/10.1002/adrr.202500072
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback2025 · 8 citations
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  4. 4DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Models2024 · 6 citations
  5. 5From Prompts to Paths: Large Language Models for Zero-Shot Planning and Simulation2025 · 1 citations