  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.
