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Related Experiment Video

Updated: May 11, 2026

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

Human-to-Robot Handover Based on Reinforcement Learning.

Myunghyun Kim¹, Sungwoo Yang¹, Beomjoon Kim²

¹Department of Electrical Engineering (Age Service-Tech), Kyung Hee University, Seoul 02447, Republic of Korea.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This summary is machine-generated.

This study enhances robot manipulator control for safer human-robot interactions (HRIs) using reinforcement learning and adaptive grasping. The system learns to exchange objects effectively, bridging simulation and real-world applications.

Keywords:

anthropomorphic gripper handover manipulator reinforcement learning

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Area of Science:

Robotics
Artificial Intelligence
Human-Robot Interaction

Background:

Advancing safe object exchange in human-robot interactions (HRIs) is crucial for collaborative robotics.
Current anthropomorphic robots require enhanced adaptability and intelligent control for diverse manipulation tasks.

Purpose of the Study:

To develop and validate a reinforcement learning (RL) framework for manipulator control in anthropomorphic robots.
To improve the safety and versatility of object exchange during human-robot interactions (HRIs).

Main Methods:

Integration of an adaptive human-robot interaction (HRI) hand for versatile grasping.
Implementation of image recognition for object identification and precise coordinate estimation.
Development of a tailored reinforcement-learning environment for dynamic scenario adaptation.

Main Results:

Demonstrated successful object identification, grasping, and manipulation through learned skills.
Validated the system's effectiveness in both simulated and real-world environments.
Showcased the robot's ability to dynamically adapt to diverse interaction scenarios.

Conclusions:

The proposed reinforcement learning approach significantly enhances manipulator control for safe HRIs.
The adaptive HRI hand and image recognition contribute to improved robotic cognitive and grasping capabilities.
The system's robustness and practicality are affirmed, paving the way for integration into advanced robotic platforms.