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Related Experiment Video

Updated: Sep 4, 2025

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

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

Published on: November 24, 2015

In situ bidirectional human-robot value alignment.

Luyao Yuan¹, Xiaofeng Gao², Zilong Zheng^1,3

¹Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.

Science Robotics

|July 20, 2022

Summary

This summary is machine-generated.

Robots can better coordinate with humans by understanding and explaining their decisions. This bidirectional communication fosters mutual understanding and aligns robot and human values in complex tasks.

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Area of Science:

Human-Robot Interaction
Artificial Intelligence
Cognitive Science

Background:

Social coordination relies on bidirectional communication, with individuals acting as both listeners and speakers.
Misalignment in human-robot values can hinder collaborative performance in complex tasks.
Robots must effectively infer human intent and explain their decision-making processes.

Purpose of the Study:

To investigate fostering effective bidirectional communication between humans and robots.
To achieve value alignment in human-robot teams through explainable AI (XAI).
To develop a system where robots predict user values and explain their decisions.

Main Methods:

Proposed an XAI system integrating a cooperative communication model to infer human values from in situ feedback.
Simulated human mental dynamics and used graphical models to predict optimal explanations.
Conducted psychological experiments to validate the computational framework.

Main Results:

Demonstrated that real-time mutual understanding between humans and robots is achievable.
Showcased the effectiveness of a learning model based on bidirectional communication.
Validated the core components of the proposed computational framework.

Conclusions:

Bidirectional communication is key for human-robot value alignment.
The developed interaction framework supports communicative XAI systems.
This approach advances future human-machine teaming systems.