The Food Process Robot Intelligent System (FPRIS) integrates a 3D-printed six-axis robotic arm with Artificial Intelligence (AI) and Computer Vision (CV) to optimize and automate the coffee roasting process. As an application of FPRIS coffee roasting, this system uses a Convolutional Neural Network (CNN) to classify coffee beans inside the roaster and control the roaster in real time, avoiding obstacles and empty spaces. This study demonstrates FPRIS’s capability to precisely control the Degree of Roasting (DoR) by combining gas and image sensor data to assess coffee bean quality. A comparative analysis between the Preliminary Coffee Sample (PCS) and Validation Coffee Sample (VCS) revealed that increasing roast intensity resulted in consistent trends for both samples, including an increase in weight loss and Gas sensor Initial Difference (GID) and a decrease in Sum of Pixel Grayscale Values (SPGVs). This study underscores the potential of FPRIS to enhance precision and efficiency in coffee roasting. Future studies will expand on these findings by testing FPRIS across various food processes, potentially establishing a universal automation system for the food industry.

Extended Reality (XR) technologies—including Virtual Reality, Augmented Reality, and Mixed Reality—are increasingly applied in the food industry to simulate sensory environments, support education, and influence consumer behavior, while robotics addresses labor shortages, hygiene, and efficiency in production. This review uniquely synthesizes their convergence through digital twin frameworks, combining XR’s immersive simulations with robotics’ precision and scalability. A systematic literature review and keyword co-occurrence analysis of over 800 titles revealed research clusters around consumer behavior, nutrition education, sensory experience, and system design. In parallel, robotics has expanded beyond traditional pick-and-place tasks into areas such as precision cleaning, chaotic mixing, and digital gastronomy. The integration of XR and robotics offers synergies including risk-free training, predictive task validation, and enhanced human–robot interaction but faces hurdles such as high hardware costs, motion sickness, and usability constraints. Future research should prioritize interoperability, ergonomic design, and cross-disciplinary collaboration to ensure that XR–robotics systems evolve not merely as tools, but as a paradigm shift in redefining the human–food–environment relationship.

Teaching-based robotic systems offer an accessible alternative to complex inverse kinematics programming for food processing automation. Traditional model-based approaches require precise system identification and analytical solutions that are challenging for custom-built robots with manufacturing tolerances and mechanical uncertainties. This study developed a custom six-degree-of-freedom robotic arm using modular brushless motors controlled via Controller Area Network communication and Robot Operating System 2, a teaching mode where users manually demonstrate trajectories that are recorded at 100 Hz. Forty-five demonstration trajectories were collected across three geometric patterns (rectangle, triangle, circle) and augmented to 270 samples. A bidirectional Long Short-Term Memory network with attention mechanism was trained to classify patterns, achieving 83.33% test accuracy and outperforming baseline deep learning models (1D-CNN: 77.78%, TCN: 66.67%, GRU: 44.44%), while being marginally exceeded by Random Forest (86.11%). Rectangle patterns showed strongest recognition (78.57% F1-score), while circle patterns achieved highest performance (91.67% F1-score). However, severe overfitting was observed, with validation accuracy peaking at 85.19% at epoch 24 before degradation, indicating insufficient dataset size despite five-fold augmentation. The results demonstrate proof-of-concept feasibility for pattern recognition from limited teaching demonstrations, providing a pathway for robotic food processing without extensive programming expertise, though larger datasets and robust feedback control strategies are required for production deployment.