Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer

05/31/2022
by   Xianjia Yu, et al.
0

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.

READ FULL TEXT

page 1

page 3

page 5

research
04/14/2022

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

Deep learning methods have revolutionized mobile robotics, from advanced...
research
04/20/2021

An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems

Autonomous systems are becoming inherently ubiquitous with the advanceme...
research
11/18/2020

FLaaS: Federated Learning as a Service

Federated Learning (FL) is emerging as a promising technology to build m...
research
07/19/2022

On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios

Federated Learning (FL) allows for collaboratively aggregating learned i...
research
11/01/2022

TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments

With the increased legislation around data privacy, federated learning (...
research
10/16/2020

Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems

In this paper, we show how the Federated Learning (FL) framework enables...
research
11/02/2022

Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness

Robotic systems are more connected, networked, and distributed than ever...

Please sign up or login with your details

Forgot password? Click here to reset