@inproceedings{discovery10197169,
       booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
       publisher = {IEEE},
           month = {August},
         address = {Yokohama, Japan},
         journal = {Proceedings - IEEE International Conference on Robotics and Automation},
            year = {2024},
           title = {Transformer-Based Prediction of Human Motions and Contact Forces for Physical Human-Robot Interaction},
           pages = {3161--3167},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          author = {Fusco, A and Modugno, V and Kanoulas, D and Rizzo, A and Cognetti, M},
             url = {https://doi.org/10.1109/ICRA57147.2024.10611211},
        abstract = {In this paper, we propose a transformer-based architecture for predicting contact forces during a physical human-robot interaction. Our Neural Network is composed of two main parts: a Multi-Layer Perceptron called Transducer and a Transformer. The former estimates, based on the kinematic data from a motion capture suit, the current contact forces. The latter predicts - taking as input the same kinematic data and the output of the Transducer - the human motions and the contact forces over a time window in the future. We validated our approach by testing the network on directions of motions that were not provided in the training set. We also compared our approach to a purely Transformer-based network, showing a better prediction accuracy of the contact forces.},
        keywords = {Training, 
Robot motion, 
Transducers, 
Accuracy, 
Human-robot interaction, 
Kinematics, 
Transformers}
}