Informed Federated Learning to Train a Robotic Arm Inverse Dynamic Model

Abstract

Access to real-world data in robotics domains is often challenging due to restrictions on data sharing and limited availability. Although privacy and intellectual property concerns are the main barriers, ensuring data access is crucial for advancing data-driven models. Specifically, machine-learning-based inverse dynamic models show promising results for nonrigid robot identification, but the data used to train them are often kept private due to intellectual property protections. Federated learning proposes a methodology to access such data without centralizing them in a single repository, thus avoiding intellectual property limitations. We propose a solution that uses federated learning to train a model from distributed data to develop a robust robotic arm inverse dynamic model. Our approach demonstrates the feasibility of using a machine learning method in which local robots train on their own data while collaborating without sharing raw information. Furthermore, we propose a novel custom aggregation method that integrates locally learned solutions from different workspaces into a single global model without requiring raw data sharing. This method improves accuracy in our federated solution by approximately 20% for the learned inverse dynamic model.