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Abstract
Effective intention recognition and trajectory tracking are critical for enabling collaborative robots (cobots) to anticipate and support human actions in Human-Robot Interaction (HRI). This study investigates the application of ensemble deep learning to classify human intentions and track movement trajectories using data collected from Virtual Reality (VR) environments. VR provides a controlled, immersive setting for precise monitoring of human behavior, facilitating robust model training. We develop and evaluate ensemble models combining CNNs, LSTMs, and Transformers, leveraging their complementary strengths. While CNN and CNN-LSTM models achieved high accuracy, they exhibited limitations in distinguishing specific intentions under certain conditions. In contrast, the CNN-Transformer model demonstrated superior precision, recall, and F1-scores in intention classification and exhibited robust trajectory tracking. By integrating multiple architectures, the ensemble approach enhanced predictive performance, improving adaptability to complex human behaviors. These findings highlight the potential of ensemble learning in advancing real-time human intention understanding and motion prediction, fostering more intuitive and effective HRI. The proposed framework contributes to developing intelligent cobots capable of dynamically adapting to human actions, paving the way for safer and more efficient collaborative workspaces.
Keywords
Human Robot Interaction, Ensemble learning, Intention recognition, Trajectory tracking.
1. Introduction
Industry 5.0 represents the next evolution in manufacturing, prioritizing human-robot collaboration (HRI) to enhance safety, efficiency, and productivity. By integrating advanced technologies such as the Internet of Things (loT), artificial intelligence (AI), digital twins, and extended reality (XR), it establishes intelligent hybrid workspaces where collaborative robots (cobots) operate alongside human workers. In these environments, cobots take on repetitive, hazardous, or physically demanding tasks, enabling humans to focus on cognitively complex activities that require creativity, problem-solving, and decision-making. This paradigm shift not only enables real-time, secure interactions between physical and virtual systems but also facilitates mass customization and drives innovation across industrial sectors [1,2] . A critical aspect of effective HRI in Industry 5.0 is contextual and situational awareness, which ensures that cobots can dynamically adapt to their environment and human counterparts. Trajectory tracking and intention recognition play a fundamental role in achieving this awareness. Intention recognition enables cobots to infer human goals and anticipate their next steps, allowing for proactive and seamless collaboration. In addition to its capacity to anticipate human goals and respond proactively, intention recognition is pivotal for ensuring...