We present CloudGripper, an open source cloud robotics testbed, consisting of a scalable, space and cost-efficient design constructed as a rack of 32 small robot arm work cells. Each robot work cell is fully enclosed and features individual lighting, a low-cost custom 5 degree of freedom Cartesian robot arm with an attached parallel jaw gripper and a dual camera setup for experimentation. The system design is focused on continuous operation and features a 10 Gbit/s network connectivity allowing for high throughput remote-controlled experimentation and data collection for robotic manipulation. CloudGripper furthermore is intended to form a community testbed to study the challenges of large scale machine learning and cloud and edge-computing in the context of robotic manipulation. In this work, we describe the mechanical design of the system, its initial software stack and evaluate the repeatability of motions executed by the proposed robot arm design. A local network API throughput and latency analysis is also provided. CloudGripper-Rope-100, a dataset of more than a hundred hours of randomized rope pushing interactions and approximately 4 million camera images is collected and serves as a proof of concept demonstrating data collection capabilities. A project website with more information is available at https://cloudgripper.org.

翻译：我们提出CloudGripper——一个开源云机器人实验平台，采用可扩展、节省空间与成本的设计，由一排32个小型机器人工作单元构成。每个工作单元全封闭，配备独立照明、低成本定制五自由度直角坐标机器人手臂（附平行夹爪）及双摄像头实验装置。系统设计以持续运行为核心，支持10 Gbit/s网络连接，可实现高吞吐量的远程控制实验与机器人操作数据采集。CloudGripper旨在构建社区实验平台，用于研究大规模机器学习及云边协同计算在机器人操作中的挑战。本文阐述系统的机械设计、初始软件栈，并评估所提机械臂运动的可重复性，同时提供局域网API吞吐量与延迟分析。我们收集了包含百小时以上随机绳索推拉交互及约400万张相机图像的CloudGripper-Rope-100数据集，作为数据采集能力的概念验证。更多信息请访问项目网站：https://cloudgripper.org。