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Cluster Haptic Texture Dataset: Haptic Texture Dataset with Varied Velocity-Direction Sliding Contacts.

Michikuni Eguchi1,2, Tomohiro Hayase3, Yuichi Hiroi4

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Summary
This summary is machine-generated.

A new haptic dataset precisely controls motion parameters for tactile stimuli. This resource aids machine learning in developing realistic virtual reality and robotic interfaces.

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Area of Science:

  • Haptic Science and Technology
  • Robotics
  • Virtual Reality

Background:

  • Existing haptic datasets lack systematic control over motion parameters, limiting analysis of tactile perception.
  • Uncontrolled exploration in current datasets hinders the development of precise haptic feedback systems.

Purpose of the Study:

  • Introduce a novel multimodal dataset for haptic texture analysis with controlled conditions.
  • Enable systematic research into how motion parameters influence tactile perception.
  • Support the development of advanced haptic rendering and recognition algorithms.

Main Methods:

  • Recorded a multimodal dataset using a 3-axis machine with a rubber tip to control sliding velocity and direction.
  • Collected data across 118 textured surfaces, nine material categories, five velocities, eight directions, and two normal forces.
  • Synchronized audio, acceleration, force, position, and visual data for 18,880 unique conditions.

Main Results:

  • Achieved 96.0% accuracy in texture recognition using convolutional neural networks.
  • Demonstrated 88.8% accuracy for velocity estimation and 78.8% for direction estimation.
  • Validated the dataset's utility for machine learning applications in haptics.

Conclusions:

  • The Cluster Haptic Texture Dataset provides a valuable resource for controlled tactile stimulus research.
  • Facilitates advancements in haptic rendering, texture recognition, and understanding human tactile perception.
  • Enables the creation of more realistic haptic interfaces for virtual reality and robotics.