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A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modules.

Viral Galayia1, Ruslan Masinjila1, Soheil Khatibi2

  • 1Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada.

Data in Brief
|February 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new tactile sensing dataset for robots, crucial for improving manipulation in complex environments. The data aids in training robots to better understand physical interactions and enhance task success rates.

Keywords:
Dynamic explorationPeg-in-holeReinforcement learningTactile sensor

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Robots require enhanced environmental awareness for unstructured tasks.
  • Limitations of vision-based sensing (occlusion, visibility) necessitate alternative approaches.
  • Tactile sensing offers a promising avenue for robotic manipulation and exploration.

Purpose of the Study:

  • To create a comprehensive dataset of tactile signals from robotic extraction tasks.
  • To facilitate research in robotic manipulation and object exploration using tactile feedback.
  • To enable pre-training of reinforcement learning models for peg-in-hole tasks.

Main Methods:

  • Utilized Bioin-Tacto modules on a robotic gripper for data acquisition.
  • Recorded sensor data including angular velocity, acceleration, magnetic fields, and pressure during peg extraction.
  • Collected 96 extraction episodes, incorporating data from a reinforcement learning agent.

Main Results:

  • The dataset captures rich tactile information during complex physical interactions.
  • Data includes multi-modal sensor readings crucial for understanding contact dynamics.
  • The dataset is suitable for pre-training machine learning models for robotic manipulation.

Conclusions:

  • The developed dataset supports the advancement of tactile sensing in robotics.
  • Pre-training with this dataset can improve robot performance in peg-in-hole tasks.
  • This resource aids in studying tactile signal inference and manipulator success rates.