TY  - GEN
UR  - https://doi.org/10.1109/ICRA57147.2024.10611211
ID  - discovery10197169
EP  - 3167
N2  - 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.
AV  - public
CY  - Yokohama, Japan
TI  - Transformer-Based Prediction of Human Motions and Contact Forces for Physical Human-Robot Interaction
Y1  - 2024/08/08/
KW  - Training
KW  -  
Robot motion
KW  -  
Transducers
KW  -  
Accuracy
KW  -  
Human-robot interaction
KW  -  
Kinematics
KW  -  
Transformers
A1  - Fusco, A
A1  - Modugno, V
A1  - Kanoulas, D
A1  - Rizzo, A
A1  - Cognetti, M
PB  - IEEE
SP  - 3161
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -