In August Anki unveiled Vector, a home robot focused on personality and character. Vector is a palm-sized bot packed with unprecedented functionality in a very computationally constrained package. Among other capabilities, he uses deep neural networks to recognize elements of interest in the world, like people, hands, and other objects. At NIPS, we will discuss how we designed and tested the neural network architectures, the unique constraints that we had to face, and the solutions we developed. Since our network will have to run on hundred of thousands of robots worldwide, we had to develop unique metrics and testing methodologies to ensure that it provides the right data to various components that depend on it. We will describe how we limited the network footprint by employing quantization and pruning, and generally running neural networks on a constrained CPU. We will also show how perception is integrated into the bigger behavioral system to create a robot that is compelling and fun to interact with.