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Related Experiment Video

Updated: Nov 30, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

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Published on: November 24, 2015

Toward Shared Autonomy Control Schemes for Human-Robot Systems: Action Primitive Recognition Using Eye Gaze Features.

Xiaoyu Wang¹, Alireza Haji Fathaliyan¹, Veronica J Santos¹

¹Biomechatronics Laboratory, Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, United States.

Frontiers in Neurorobotics

|November 12, 2020

Summary

This summary is machine-generated.

This study uses eye gaze to improve robot control for individuals with upper limb impairments. Gaze-based action recognition enhances shared autonomy, making teleoperated robots more intuitive for daily living assistance.

Keywords:

action primitive recognition activities of daily living eye gaze gaze-object angle human-robot systems recurrent neural network shared autonomy

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Area of Science:

Robotics
Human-Computer Interaction
Assistive Technology

Background:

Teleoperated robots can enhance functional independence for individuals with upper limb impairments.
Current robot control methods lack intuitiveness for operators.
Shared autonomy control schemes require effective human intent recognition.

Purpose of the Study:

To develop an intuitive robot control system using eye gaze for inferring human intent.
To advance action recognition in shared autonomy by incorporating novel gaze-related features.
To improve the functional independence of individuals with upper limb impairments through enhanced robot assistance.

Main Methods:

Introduced a classifier for recognizing low-level action primitives (verb, target object, hand object).
Incorporated novel three-dimensional gaze-related features into the classifier.
Trained a recurrent neural network on gaze data and tested it on three activities, including making a powdered drink.

Main Results:

Achieved 77% accuracy for verb recognition and 83% for target object recognition in a representative activity.
Demonstrated modest generalizability of the action primitive classifier across untrained activities.
Identified gaze object angle and its rate of change as key features for accurate recognition and reduced latency.

Conclusions:

Eye gaze provides a natural and effective input for inferring human intent in robot control.
The proposed gaze-based action recognition system enhances shared autonomy and reduces observational latency.
This approach holds promise for improving the usability of teleoperated robots for daily living assistance.