Distributed AI for Robotics

Abstract

Robot learning primarily relies on centralized training. While it provides the infrastructure, centralization limits parallel and collaborative learning among robots and place significant computational load on the central server, indicating the need for federated learning (FL) in context of multi-robot training. However, robots trained in a federated setup are subjected to non-independent and identically distributed data (non-IID), resulting in degraded model performance. This extended abstract presents the current state of research aimed at improving robot learning under non-IID conditions in FL. In this regard, this work provides an initial comparative analysis of robot learning methods in centralized and federated training setups, with an emphasis on the impact of non-IID data on learning behaviour in a simulation environment. The results highlight the differences in learning stability across algorithms and present the influence of non-IID goal distributions on performance.