Abstract

This article presents the integration of advanced artificial intelligence algorithms applied to collaborative robots (cobots) in five strategic sectors: underground mining, geriatric medicine, precision agriculture, port logistics, and high-precision manufacturing. It proposes the use of Graph Convolutional Neural Networks (Graph-CNNs) to model spatiotemporal motion; Multimodal Transformers to fuse heterogeneous sensor data; Soft Actor-Critic with Constrained Policy Optimization (SAC-RCPO) for safe and adaptive control; and TactileGAN to generate realistic haptic feedback. Within this framework, multimodal AI improves operational efficiency, task accuracy, and the quality of human-robot interaction. In practice, it contributes to risk reduction in hazardous environments such as mining and logistics, improves accuracy in agricultural and manufacturing tasks, and fosters greater acceptance among older adults in healthcare settings. A review of existing literature and simulations in agriculture and mining confirms performance improvements and supports the feasibility of integrating algorithms into collaborative robotic systems. © 2025 IEEE.