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Intent-driven LLM ensemble planning for flexible multi-robot manipulation.

Cansu Erdogan1, Cesar Alan Contreras1, Alireza Rastegarpanah1,2

  • 1Extreme Robotics Lab, School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom.

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Summary
This summary is machine-generated.

This study introduces an intent-driven task planning system for multi-robot cells, using large language models (LLMs) to generate robot action sequences from human intent. The system enhances planning accuracy and efficiency for complex manipulation tasks.

Keywords:
human–robot interactionintent recognitionlarge language modelsmulti-robot disassemblytask planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Complex multi-action manipulation in multi-robot systems requires sophisticated task planning.
  • Bridging human intent with robot execution is a significant challenge in autonomous systems.

Purpose of the Study:

  • To develop an intent-driven task planning system for heterogeneous multi-robot cells.
  • To generate precedence-consistent robot-action sequences from natural-language human intent.

Main Methods:

  • A pipeline integrating perception-to-text encoding, large language models (LLMs) for action sequence generation, an LLM-based verifier, and a deterministic consistency filter.
  • Evaluation on a collaborative EV battery dismantling task for recycling.

Main Results:

  • The ensemble-with-verification approach improved full-sequence correctness from 0.761 to 0.824 and next-object correctness from 0.866 to 0.894.
  • Human participant experiments showed low user effort and reduced time to execution.

Conclusions:

  • The developed system reliably maps operator intent to safe multi-robot plans.
  • The ensemble-with-verification method enhances the performance and usability of robot task planning.