Informed Federated Learning to Train a Robotic Arm Inverse Dynamic Model
Abstract
Access to real-world robotics data is often constrained by data-sharing restrictions and intellectual-property concerns. This work proposes an informed federated-learning approach to train a robotic-arm inverse dynamic model from distributed datasets without centralising the data. The approach improves the learned model while preserving the ownership and privacy of participating data sources.
Brayan Alfonso Valencia Vidal
Postdoctoral Researcher
Postdoctoral researcher in the Applied Computational Neuroscience Group, working on bio-inspired robotics and control.
Niceto R. Luque
Associate Professor
Associate Professor at the Department of Computer Engineering, Automation and Robotics and Principal Investigator at the Applied Computational Neuroscience Group.
Eduardo Ros
Full Professor
Full professor in computer architecture, principal investigator at the Computational Neuroscience and Neurorobotics Lab and principal investigator of the VALERIA lab of the University of Granada.
Francisco Barranco
Associate Professor
Associate Professor at the Department of Computer Engineering, Automation and Robotics, Principal Investigator at the Applied Computational Neuroscience Group and the Computer Vision and Robotics Lab of the University of Granada.