  The role of deep learning (DL) in robotics has significantly deepened over
the last decade. Intelligent robotic systems today are highly connected systems
that rely on DL for a variety of perception, control, and other tasks. At the
same time, autonomous robots are being increasingly deployed as part of fleets,
with collaboration among robots becoming a more relevant factor. From the
perspective of collaborative learning, federated learning (FL) enables
continuous training of models in a distributed, privacy-preserving way. This
paper focuses on vision-based obstacle avoidance for mobile robot navigation.
On this basis, we explore the potential of FL for distributed systems of mobile
robots enabling continuous learning via the engagement of robots in both
simulated and real-world scenarios. We extend previous works by studying the
performance of different image classifiers for FL, compared to centralized,
cloud-based learning with a priori aggregated data. We also introduce an
approach to continuous learning from mobile robots with extended sensor suites
able to provide automatically labeled data while they are completing other
tasks. We show that higher accuracies can be achieved by training the models in
both simulation and reality, enabling continuous updates to deployed models.
