October 13, 2025Open Access

RoboDexVLM: Visual Language Model-Enabled Task Planning and Motion Control for Dexterous Robot Manipulation

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RoboDexVLM significantly enhances dexterous robot manipulation in long-horizon scenarios, with improved task execution.
The framework employs visual language models to interpret natural language commands for complex tasks.
A novel grasp perception algorithm aids in robust detection of diverse objects, enhancing the robot's dexterity.
Experimental validation shows that RoboDexVLM excels in challenging environments, demonstrating its potential for practical applications.

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Innovative framework improves task planning and grasp detection in robots, indicating greater adaptability.

Liu et al. (2025) studied this question.

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