Content area
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.
Details
Accuracy;
Collaboration;
Deep learning;
Adaptability;
Trends;
Artificial neural networks;
Robots;
Data processing;
Manufacturing;
Machine learning;
Tracking;
Time series;
Virtual reality;
Robustness;
Artificial intelligence;
Trajectories;
Recognition;
Neural networks;
Decision making;
Human motion;
Algorithms;
Human engineering;
Real time;
Ensemble learning;
Industry 5.0;
Human behavior