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Vision-Based Human-Robot Handover System with Reinforcement Learning.

Weiliang Cao¹, Zhenwei Cao¹, Yong Song²

¹School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This summary is machine-generated.

This study introduces a vision-based human-robot handover system (VHS) to address collaboration challenges. The novel approach enhances robotic arm control and learning for smoother, more efficient handovers.

Keywords:

collaboration handover human–robot simulation vision-based

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Area of Science:

Robotics
Human-Robot Interaction
Computer Vision

Background:

Human-robot collaboration presents significant challenges, particularly in handover tasks.
Effective handover control requires sophisticated perception and adaptive robotic arm manipulation.
Existing methods often lack robustness and efficient learning strategies.

Purpose of the Study:

To propose a novel three-step vision-based human-robot handover system (VHS).
To enhance adaptive control of robotic arms using vision inputs for environmental perception.
To improve learning strategies for policy exploration and convergence in handover tasks.

Main Methods:

A three-step vision-based human-robot handover system (VHS) was developed.
Vision inputs were utilized for environmental perception and adaptive robotic arm control.
A three-step behavior cloning learning strategy was implemented.
A modified Temporal Difference (TD) loss function based on transfer models was proposed for training.

Main Results:

The proposed VHS demonstrated substantial enhancements in experimental validation.
Comparative analysis in a simulation environment showed significant improvements.
The system utilized a realistic dynamic hand model for accurate testing.
The modified TD loss function improved policy exploration and convergence.

Conclusions:

The developed vision-based human-robot handover system effectively addresses handover control challenges.
The integration of vision-based perception and behavior cloning offers a promising approach for human-robot collaboration.
The modified TD loss function contributes to improved learning efficiency and system performance.