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May 25, 2026Robot learning.

Robotic assembly via self-prompt Segment Anything Model and discrete prompt optimization

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Authors

QGQi GuoNorthwestern Polytechnical UniversityXLXing LiuNorthwestern Polytechnical UniversityHCHaitao ChangNorthwestern Polytechnical University

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Implication

Randomized trial demonstrates improved assembly instructions generation using MLLMs in robotics, suggesting enhanced efficiency and stability.

Key Points

  • This research aims to improve robotic assembly processes by generating structured assembly instructions from visual inputs using MLLMs.
  • Development of a Perception-Recognition-Planning-Action framework for robotic assembly.
  • Use of a self-prompt Segment Anything Model to create structured visual representations.
  • Implementation of a discrete prompt optimization mechanism for refining input prompts.
  • Prompt optimization mechanism reduces average reasoning attempts by 48%.
  • Achieves 95% stability in part recognition across varied assembly scenarios.
  • Enhances the generation of feasible assembly action sequences for robotic execution.

Cite This Study

Guo et al. (2026) studied this question.

synapsesocial.com/papers/6a13e71a0e02ee3982d31c4dhttps://doi.org/10.55092/rl20260018
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Also Consider

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

  1. 1LLM-Based Multimodal Prompting for Adaptive Robot Control in Production Systems2026
  2. 2A Neuro-Symbolic Framework for LLM-Driven Task Planning and Execution in Industrial Assembly2026
  3. 3Prompt Selection and Augmentation for Few Examples Code Generation in Large Language Model and its Application in Robotics Control2024
  4. 4RoboMP$^2$: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models2024
  5. 5Empowering Large Language Models on Robotic Manipulation with Affordance Prompting2024 · 5 citations