Single candidate optimizer based context-aware human motion prediction and action planning for human-robot collaboration using coupled modular transformer network

Accurate and timely prediction of human motion is crucial for enabling proactive and safe robotic assistance in Human-Robot Collaboration (HRC) scenarios. This paper introduces a new model, coupled with their modular uncertainty-aware decision transformer network with single candidate optimizer (CMUADTN-SCO) to predict human motion with high accuracy and efficiency. Using a large-scale collection of labeled videos of disassembly tasks processes on 30 FPS, 1920 × 1080, the system has had to use the powerful pre-processing methods of spatial resizing and temporal data augmentation to form variable length motion clips. The framework involves an Enhanced VGG19-Graph Attention Network (E-VGG19-GAN) to extract spatial features and make relational decisions which are then fed through the CMUADTN architecture that consists of a Coupled Modular Neural Network (CMNN) to classify motion and an Uncertainty-Aware Decision Transformer (UNREST) to make future predictions. To enable real-time applicability, the Single Candidate Optimizer (SCO) method enhances hyperparameter tuning, optimizing model responsiveness and efficiency. The proposed method surpasses existing state-of-the-art techniques, achieving 98.4% accuracy, 97.9% precision, 97.7% recall, 97.8% F1-score, and 98.9% AUC. These results confirm the framework’s ability to deliver accurate, confident, and safe collaborative behavior recognition in dynamically evolving HRC environments.