Despite recent successes of reinforcement learning (RL) in simulated environments, deploying or training algorithms in the real-world remains a challenge due to the significant cost of experimentation and limited datasets. While insights gained in simulation do not necessarily translate to real robots, we aim to close the gap between simulation and the real-world by offering participants the opportunity to submit their algorithm to a robotics benchmark in the cloud. This will allow teams to gather hundreds of hours of real robot data with minimal effort and submission to our cloud benchmark is as easy as using a simulator. Simulators, easy to use interfaces and large real-world datasets for pretraining are available. Show that your algorithm is practical by solving the tasks on different levels in the real-world and win prizes!