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A deep neural network for tactile perception in open scenes.

Huirong Fang1,2, Qianhui Yang3, Kunhong Liu3,4

  • 1School of Electronic Information, Zhangzhou Institute of Technology, Zhangzhou 363000, China.

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|May 9, 2025
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Summary

This study introduces a new method for robot tactile material recognition in open environments, overcoming data variations. The multi-receptive field attention enhancement network (MRFE) improves robot perception despite changes in conditions.

Keywords:
KinesiologyRobotics

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Materials Science

Background:

  • Tactile perception is crucial for robots to interact with their environment.
  • Real-world robotic applications face challenges like unexpected environmental changes, leading to data variations and difficulties in tactile perception.
  • Existing tactile perception studies often focus on enclosed environments, limiting applicability to open scenes.

Purpose of the Study:

  • To address the challenge of tactile material recognition in open scenes for robots.
  • To develop a method that can handle data drift caused by variations in external conditions.
  • To enhance the robustness of robot tactile perception in dynamic environments.

Main Methods:

  • Construction of a cross-batch tactile dataset to simulate open-scene conditions.
  • Proposal of a multi-receptive field attention enhancement network (MRFE) for tactile material recognition.
  • Experimental comparison with other machine learning algorithms.

Main Results:

  • The proposed MRFE method effectively overcomes data drift issues.
  • The system demonstrates improved tactile material recognition despite variations in posture, contact force, sliding velocities, exploratory motions, and assembly conditions.
  • Experimental results show superior performance compared to existing machine learning algorithms.

Conclusions:

  • The MRFE network provides a robust solution for tactile material recognition in open robotic scenes.
  • This approach enhances robot adaptability and reliability in real-world, dynamic environments.
  • The study contributes to advancing robot tactile perception capabilities for practical applications.