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Enhancing Robotic Grasping Detection Using Visual-Tactile Fusion Perception.

Dongyuan Zheng1,2, Yahong Chen1

  • 1School of Artificial Intelligence, Lishui University, Lishui 323000, China.

Sensors (Basel, Switzerland)
|January 28, 2026
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Summary
This summary is machine-generated.

This study introduces a new robotic grasping detection method using combined visual and tactile perception. The novel approach significantly improves grasping accuracy by predicting grasp stability.

Keywords:
deep learninggrasp stabilityrobotic graspingtactile sensingvisual–tactile fusion

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Area of Science:

  • Robotics and Artificial Intelligence
  • Sensor Fusion and Perception

Background:

  • Tactile perception is increasingly integrated into robotics for tasks like object recognition and grasp assessment.
  • Current methods often limit tactile perception's role, overlooking its potential for enhancing grasp detection.
  • Rethinking the synergy between visual and tactile data is crucial for advancing robotic manipulation.

Purpose of the Study:

  • To propose a novel robotic grasping detection method leveraging both visual and tactile perception.
  • To enhance the accuracy of robotic grasping detection through a fused perception approach.
  • To introduce a Grasp Stability Prediction Module (GSPM) for improved grasp stability assessment.

Main Methods:

  • Construction of a visual-tactile dataset incorporating grasp stability for potential grasping positions.
  • Development of a Grasp Stability Prediction Module (GSPM) to generate grasp stability probability maps.
  • Fusion of the grasp stability map with corresponding color images as input for the grasp detection network.

Main Results:

  • The visual-tactile fusion method significantly enhances robotic grasping detection accuracy.
  • The Grasp Stability Prediction Module provides valuable prior knowledge for grasp detection.
  • Experimental validation confirms the effectiveness of the proposed approach.

Conclusions:

  • Combining visual and tactile perception offers a superior approach to robotic grasping detection.
  • The novel method, integrating a GSPM, demonstrates a significant improvement in grasping accuracy.
  • This research opens new avenues for advanced robotic manipulation through enhanced perception.