论文标题
通过连续的SIM到现实转移，在自动移动机器人中终生联合学习
Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer
论文作者
论文摘要
在过去的十年中，深度学习（DL）在机器人技术中的作用已大幅加深。当今的智能机器人系统是高度连接的系统，依靠DL来进行各种感知，控制和其他任务。同时，作为车队的一部分，自动驾驶机器人越来越多地部署，并且机器人之间的协作成为更相关的因素。从协作学习的角度来看，联合学习（FL）可以以分布式，隐私的方式对模型进行连续培训。本文着重于基于视觉的移动机器人导航的障碍。在此基础上，我们探索了FL对于移动机器人分布式系统的潜力，可以通过在模拟和真实世界中的机器人的参与来连续学习。我们通过研究FL的不同图像分类器的性能，与基于云的集中学习和先验汇总的数据相比，我们扩展了以前的工作。我们还介绍了一种方法，该方法可以从移动机器人中进行持续学习，其中扩展传感器套件能够在完成其他任务时提供自动标记的数据。我们表明，可以通过在模拟和现实中训练模型来实现更高的精度，从而使部署模型的连续更新能够进行。
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.
