Neural-Network-Driven Intention Recognition for Enhanced Human–Robot Interaction: A Virtual-Reality-Driven Approach

Intention recognition in Human–Robot Interaction (HRI) is critical for enabling robots to anticipate and respond to human actions effectively. This study explores the application of deep learning techniques for the classification of human intentions in HRI, utilizing data collected from Virtual Reality (VR) environments. By leveraging VR, a controlled and immersive space is created, where human behaviors can be closely monitored and recorded. Ensemble deep learning models, particularly Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformers, are trained on this rich dataset to recognize and predict human intentions with high accuracy. While CNN and CNN-LSTM models yielded high accuracy rates, they encountered difficulties in accurately identifying certain intentions (e.g., standing and walking). In contrast, the CNN-Transformer model outshone its counterparts, achieving near-perfect precision, recall, and F1-scores. The proposed approach demonstrates the potential for enhancing HRI by providing robots with the ability to anticipate and act on human intentions in real time, leading to more intuitive and effective collaboration between humans and robots. Experimental results highlight the effectiveness of VR as a data collection tool and the promise of deep learning in advancing intention recognition in HRI.