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Gaze-Based Intention Estimation for Shared Autonomy in Pick-and-Place Tasks.

Stefan Fuchs1, Anna Belardinelli1

  • 1Honda Research Institute Europe, Offenbach, Germany.

Frontiers in Neurorobotics
|May 3, 2021
PubMed
Summary
This summary is machine-generated.

Robots can predict human intentions early in teleoperated tasks by analyzing eye movements. This allows for faster, more reliable robotic assistance in shared autonomy systems.

Keywords:
Hidden Markov Modelseye trackingeye-hand coordinationhuman-robot interactionintention recognitionshared autonomyteleoperation

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Shared autonomy requires robots to reliably recognize human intentions for effective teleoperation.
  • Eye movements are early and reliable indicators of cognitive processes during manual tasks.

Purpose of the Study:

  • To analyze human behavior in simulated teleoperated pick-and-place tasks.
  • To develop a model for early estimation of proximal human intention.

Main Methods:

  • Experimentation with simulated teleoperated pick-and-place tasks.
  • Analysis of eye movement scan paths.
  • Development and evaluation of Gaussian Hidden Markov Models (GHMMs) for intention prediction.

Main Results:

  • Scan paths significantly correlate with current human intentions.
  • Scene-specific and action-specific GHMMs demonstrated high prediction performance.
  • Models generalized effectively to new users and different spatial arrangements.

Conclusions:

  • Eye movements provide valuable cues for early intention recognition in shared autonomy.
  • The developed models can be leveraged to enhance cooperative robotic systems.
  • Eye movements reflect generalizable higher-level planning strategies applicable across various object configurations.