Open Bot. Turning Smartphones into Robots. Open Bot consists of two components in an expensive robot body and a smartphone that acts as its brain. Smartphones feature powerful processors, a comprehensive sensor suite, and abundant communication interfaces. Open Bot becomes more capable every year as mobile phones acquire faster processors, better sensors, and new communication capabilities. Most people already own a smartphone, producing the cost of the robot to only its body. We have designed a body for a wheel robot which relies on low cost readily available hobby hardware. The total cost of building such a robot is only $50. We release our design for free to the community. The body consists of several 3D printed parts. The bottom shell houses all the electronics. It is sealed by a top cover with a universal smartphone holder. Assembly of the robot is easy and takes 30 to 60 minutes. It only requires basic tools such as a screwdriver, pliers, and a soldering iron. When using our custom PCB, the assembly time is reduced to about 15 minutes. As the smartphone is connected, it imparts its capabilities to the robot. In the following, we show quantitative results for two navigation tasks, person following, and autonomous navigation. Person following. In this task, the robot is following a person via visual servoing. We use an off-the-shelf SSD object detector with a mobile net backbone trained on the Cocoa dataset. At every frame, we only consider detections of the person class and pick the one with the highest confidence score as target. As long as a person is detected, we apply full throttle and differential steering proportional to the offset between the center of the frame and the bounding box. Our robot is able to follow a person reliably in unstructured outdoor environments. We also conducted extensive quantitative experiments in a controlled indoor environment, and the results are reported in our paper. We tested 10 different smartphones and found that even mid-range phones are capable of following the person. Autonomous navigation. We designed a compact driving policy and compared it to two baselines. Our network achieves similar driving performance while using about one order of magnitude fewer parameters. We investigate generalization to novel phones. Our driving policy, which was trained with data from one phone and augmentation, generalizes to other phones. Similarly, we investigate generalization to novel robot bodies. Our driving policy, which was trained with data from one body and injected noise, generalizes to other bodies. Our driving policy also generalizes to changes in the environment. For example, our robot is able to navigate along a novel route, which was not part of the training set. During data collection, our driving policy was exposed to static obstacles in the form of office chairs. These chairs were randomly placed along the corridors. We find that our driving policy generalizes to novel obstacle configurations. What is more, our driving policy generalizes to novel obstacles, which are visually very different from the one seen during training. The potted plants differ from the office chairs in shape, texture, and color. The low image resolution, aggressive down sampling, and small number of parameters in our network may serve as a natural regularization that prevents the network from overfitting to specific obstacles. Even dynamic obstacles can be avoided since the network operates on single frames. Static and dynamic obstacles are treated on the same basis. We also conjecture that the network has learned some robustness to motion blur due to vibrations of the vehicle. Finally, we deploy our driving policy in a novel environment within the same building. This environment is on a different floor and features a different corridor layout. Our driving policy is also able to successfully control the robot in this case. This work aims to address two key challenges in robotics, accessibility, and scalability. We are releasing all materials for free to democratize robotics for education and research around the world. Get started now at OpenBot.org.