Enhancing Mobile Robotics with Federated Learning: A Collaborative Approach to Supervised Semantic Mapping in Logistic Environments

Semantic mapping has many use cases in mobile robotics. For example, in logistic environments, it is applied to find and transport loading goods or in mobile manipulation to identify objects to grasp, and it often relies on neural networks to accomplish image detection and segmentation for semantic mapping. Data sets are usually required to train those neural networks, which are difficult to generate for many organizations since their creation requires qualified personnel. This paper demonstrates how federated learning, combined with supervised neural networks, effectively reduces the effort for semantic map training while maintaining data sovereignty. It allows organizations to facilitate their individual training data generation efforts to build semantic maps by collaborating with other organizations without sharing their training data and, therefore, not losing their data sovereignty. At the same time, they can reduce their individual effort to generate and train data.