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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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EEG-controlled tele-grasping for undefined objects.

Minki Kim1, Myoung-Su Choi2, Ga-Ram Jang2

  • 1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.

Frontiers in Neurorobotics
|January 8, 2024
PubMed
Summary

This study introduces a robot grasping system using real-time electroencephalography (EEG) and shared autonomy. It enables human operators to make critical decisions for grasping uncertain objects, enhancing robotic capabilities in unstructured environments.

Keywords:
brain-machine (computer) interfaceelectroencephalogram (EEG)human-robot collaborationsteady-state visual evoked potential (SSVEP)telerobotics and teleoperation

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Area of Science:

  • Robotics
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Fully autonomous grasping struggles with undefined objects in unstructured environments.
  • Real-time human decision-making is crucial for uncertain robotic tasks.
  • Teleoperation systems require intuitive human control interfaces.

Purpose of the Study:

  • To develop a teleoperation system for robot grasping of undefined objects.
  • To integrate real-time electroencephalography (EEG) measurements for human control.
  • To implement shared autonomy allowing human decisions throughout the grasping process.

Main Methods:

  • A teleoperation system utilizing shared autonomy and real-time EEG.
  • Steady-state visually evoked potentials (SSVEP) generated by six flickering blocks for human input.
  • Division of the grasping task into predefined substeps (approach, posture/force selection, grasp, transport, release).
  • A graphical user interface (GUI) displaying task status and control options.

Main Results:

  • Demonstrated successful tele-grasping of various objects using human decisions via EEG.
  • Enabled human control over gripper 3D movement, grasping posture (4 options), and grip force (3 levels).
  • The system effectively integrates human decision-making into sequential robotic tasks.

Conclusions:

  • The developed EEG-based teleoperation system enhances robot grasping capabilities for undefined objects.
  • Shared autonomy allows for flexible human intervention in complex robotic tasks.
  • This approach is adaptable to other sequential teleoperation tasks requiring intricate human decisions.