We introduce OpenBot-Fleet, a comprehensive open-source cloud robotics system
for navigation. OpenBot-Fleet uses smartphones for sensing, local compute and
communication, Google Firebase for secure cloud storage and off-board compute,
and a robust yet low-cost wheeled robot toact in real-world environments. The
robots collect task data and upload it to the cloud where navigation policies
can be learned either offline or online and can then be sent back to the robot
fleet. In our experiments we distribute 72 robots to a crowd of workers who
operate them in homes, and show that OpenBot-Fleet can learn robust navigation
policies that generalize to unseen homes with >80% success rate. OpenBot-Fleet
represents a significant step forward in cloud robotics, making it possible to
deploy large continually learning robot fleets in a cost-effective and scalable
manner. All materials can be found at this https URL A video is
available at this https URL

OpenBot-Fleet 是一个开源的云机器人系统，利用智能手机感知、本地计算和通信，Google Firebase 进行安全的云存储和离线计算，以及强大且低成本的轮式机器人在真实环境中执行任务，并在云中学习导航策略，实验表明 OpenBot-Fleet 可在各种未知环境下成功导航，是云机器人领域的重要进展。

OpenBot-Fleet: 实际机器人的集体学习系统

OpenBot-Fleet: A System for Collective Learning with Real Robots

Humans are capable of learning a new behavior by observing others to perform
the skill. Similarly, robots can also implement this by imitation learning.
Furthermore, if with external guidance, humans can master the new behavior more
efficiently. So, how can robots achieve this? To address the issue, we present
a novel framework named FIL. It provides a heterogeneous knowledge fusion
mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL
is proposed. It enables the cloud to fuse heterogeneous knowledge from local
robots and generate guide models for robots with service requests. After that,
we introduce a knowledge transfer scheme to facilitate local robots acquiring
knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge
from other robots to increase its imitation learning in accuracy and
efficiency. Compared with transfer learning and meta-learning, FIL is more
suitable to be deployed in cloud robotic systems. Finally, we conduct
experiments of a self-driving task for robots (cars). The experimental results
demonstrate that the shared model generated by FIL increases imitation learning
efficiency of local robots in cloud robotic systems.

本文介绍了一种新颖的基于云机器人系统的知识融合和转移框架 FIL，该框架通过融合来自本地机器人的异构知识并生成服务请求机器人的引导模型来实现云机器人系统中的姿态学习，同时还引入了一种知识转移方案来促进本地机器人从云中获取知识，最终实验结果表明 FIL 可以提高云机器人系统中本地机器人的姿态学习效率。