Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things

TL;DR

This paper investigates federated learning for vision-based obstacle avoidance in mobile robots, demonstrating improved navigation performance and privacy benefits through collaborative, decentralized training in real-world indoor environments.

Contribution

It is the first to apply federated learning to vision-based robotic navigation and compares its effectiveness to centralized training in real-world scenarios.

Findings

Federated learning outperforms centralized training in navigation accuracy.

Sim-to-real transfer enhances real-world robot performance.

Federated approach preserves data privacy at the edge.

Abstract

Deep learning methods have revolutionized mobile robotics, from advanced perception models for an enhanced situational awareness to novel control approaches through reinforcement learning. This paper explores the potential of federated learning for distributed systems of mobile robots enabling collaboration on the Internet of Robotic Things. To demonstrate the effectiveness of such an approach, we deploy wheeled robots in different indoor environments. We analyze the performance of a federated learning approach and compare it to a traditional centralized training process with a priori aggregated data. We show the benefits of collaborative learning across heterogeneous environments and the potential for sim-to-real knowledge transfer. Our results demonstrate significant performance benefits of FL and sim-to-real transfer for vision-based navigation, in addition to the inherent privacy-preserving nature of FL by keeping computation at the edge. This is, to the best of our knowledge, the first work to leverage FL for vision-based navigation that also tests results in real-world settings.