Human-Robot Hybrid Manufacturing

XR training and support enhancing Human-Robot Collaboration

The manufacturing sector is increasingly defined by the synergy between human expertise and advanced robotics. To successfully navigate this transition toward Industry 5.0, operators need immersive, risk-free environments that bridge the gap between complex machine theory and practical mastery.

MOTIVATE XR Pilot 5 empowers the modern workforce through:

Adaptive Learning: High-fidelity virtual environments that accelerate skill acquisition.
Risk-Free Mastery: Safe simulations for mastering robotic interaction without errors.
Human-Centric Systems: Seamless synchronisation between workers and intelligent robots.
Data-Driven Support: Real-time analytics for continuous guidance and feedback.

Partner involved:

OVERVIEW & GOALS OF PILOT

Focus: Training and supporting workers to interact with physical equipment and manufacturing robots through AI-driven Digital Twins and XR.

Process & Impact: This pilot, led by BI-REX, aims to explore how human operators use and integrate XR tools for assembly purposes, interacting with intelligent mobile cyber-physical systems (Mobots).

Validation & Innovation: The pilot serves as a critical validation for the MOTIVATE XR platform within high-stakes manufacturing scenarios. It integrates an anthropomorphic robot into a collaborative assembly use case, testing the platform’s ability to handle complex kinematics and real-time human-robot interaction. The result is a robust, scalable training and support solution that aims to move beyond the state-of-the-art (SOTA) in industrial education and support tools.

Advantages for End User & Innovation Potential

Operational Resilience: Improving the scalability and efficiency of manufacturing processes through faster learning cycles.
Higher Productivity and Quality: Real-time feedback and gesture-based controls improve precision in collaborative tasks, optimising cycle times through better operator-robot coordination
Scalable Knowledge Transfer: Creating a high-fidelity environment that supports the rapid adaptation of technical staff to new robotic tasks.
Data-driven Optimisation: captures performance metrics during XR sessions for predictive analytics and continuous improvement, enabling personalisation of training paths.

Achievement & Results

Training Context & Objectives: To assist inexperienced operators with complex mechanical assembly, BI-REX developed a collaborative test bed featuring a 1:1 scale additive-manufactured gearbox and interactive digital instructions. By combining step-by-step video guidance with a robotic arm that delivers components, the system aims to boost operator confidence, minimise errors, and streamline the workflow. Testing with a diverse group of volunteers provided critical feedback on the user experience, paving the way for a more robust, user-centric solution that integrates immersive technologies and collaborative robotics in line with Industry 5.0 principles.

XR Training Implementation: To prepare for beta testing, BI-REX developed an immersive XR training scenario using KAYROX software, using 3D models and technical documentation to guide the assembly of a complex gearbox. During the trial, 14 participants with no prior knowledge of the component successfully completed the assembly by following XR instructions and interacting with a touch-sensitive collaborative robot. Pre- and post-session surveys revealed high levels of user engagement and positive feedback across diverse professional backgrounds. These results confirm the effectiveness of XR-driven training and provide a roadmap for future versions, which will feature full synchronisation between robotic hardware and the digital environment.

What’s Coming Next

The next phase of the project will concentrate on achieving a deeper integration between Mobot systems, which combine autonomous mobile robots and collaborative robots, and XR headsets through the Hand Gesture Recognition libraries provided by project partners. This integration is designed to give operators the ability to interact with industrial robots in a secure and simplified manner, making collaboration more fluid and interactive. Augmented reality feedback will play a key role in this process, offering real-time visual information such as the robot’s footprint and highlighting safe zones for the operator, thereby improving safety and confidence in human-robot interaction.

In addition, BI-REX places strong emphasis on establishing mechanisms for collecting data and processing metrics during XR sessions. These insights will enable continuous improvement and support the personalisation of training paths, ensuring that learning experiences are tailored to individual needs and contribute to greater efficiency and adaptability within the manufacturing environment. This approach represents a significant step toward creating a smart, human-centric ecosystem where technology enhances safety, productivity, and innovation.

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