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Related Experiment Video

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A Systematic Review of Safety-Driven Approaches in Human-Robot Collaborative Systems.

Akhtar Khan1, Maaz Akhtar2, Sheheryar Mohsin Qureshi3

  • 1Department of Electrical Engineering, Iqra Nations University, Peshawar 25000, Pakistan.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary

Generative AI enhances human-robot collaboration (HRC) safety by making systems proactive and interpretable. Challenges remain in real-time performance and ethical assurance for trustworthy robotic systems.

Keywords:
Generative Adversarial NetworksPRISMAgenerative AIlarge language modelsvariational auto encoders

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Area of Science:

  • Robotics and Artificial Intelligence
  • Human-Robot Collaboration (HRC)
  • Generative Artificial Intelligence (GenAI)

Background:

  • Human-robot collaboration (HRC) is rapidly advancing, driven by the integration of robotics and generative artificial intelligence (GenAI).
  • Generative models are crucial for enhancing safety, trust, and adaptability in collaborative robotic systems.
  • A systematic review of 103 studies was conducted to analyze the role of GenAI in HRC.

Purpose of the Study:

  • To systematically review the literature on GenAI applications in HRC safety, trust, and adaptability.
  • To identify key themes and challenges in using generative models for collaborative robotics.
  • To propose a framework and roadmap for developing certifiable, trustworthy HRC systems.

Main Methods:

  • A PRISMA-based systematic review methodology was employed.
  • Analysis focused on studies utilizing generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, and Large Language Models (LLMs).
  • Identified four major themed areas of GenAI-based safety frameworks.

Main Results:

  • GenAI transforms robotic safety from reactive to proactive, contextual, and interpretable systems.
  • Key GenAI applications include data-driven hazard synthesis, human motion prediction, adaptive risk control, and trust-aware interaction explanations.
  • Identified challenges include real-time performance, interpretability, standardization, and ethical considerations.

Conclusions:

  • Generative models offer a foundation for creating transparent, adaptive, and trustworthy collaborative robotic systems.
  • A proposed taxonomy links generative modeling to physical, cognitive, and ethical aspects of HRC safety.
  • A roadmap is provided for developing certifiable hybrid systems combining generative foresight with deterministic control.