The main goal of this talk is to illustrate how machine learning can start to address some of the fundamental perceptual and control challenges involved in building intelligent robots. I’ll start by discussing the new high speed autonomous “rally car” platform built in collaboration with several labs at Georgia Tech, and discuss an off-road racing task that requires impressive sensing, speed, and agility to complete. I will describe two approaches to this problem, one based on model predictive control and one based on learning deep policies that directly map images to actions. Along the way I’ll introduce new tools from reinforcement learning, imitation learning, and online learning and show how theoretical insights help us to overcome some of the practical challenges involved in learning on a real-world platform. I will conclude by discussing ongoing work in my lab related to machine learning for robotics.