Introduction to Robot Learning

12 units · Graduate

Fall and Spring: 12 units Robots need to make sequential decisions to operate in the world and generalize to diverse environments. How can they learn to do so? This is what we call the "robot learning" problem, and it spans topics in machine learning, deep learning, visual learning, and reinforcement learning. In this course, we will learn the fundamentals of topics in machine/deep/visual/reinforcement learning and how such approaches are applied to robot decision-making. We will study fundamentals of 1) machine/deep learning with an emphasis on approaches relevant to robotics; 2) reinforcement learning: model-based, model-free, on-policy (e.g., policy gradients), off-policy (e.g., Q-learning), offline, etc.; 3) imitation learning: behavior cloning, DAgger, inverse RL, etc.; and 4) visual learning geared towards decision making including topics like generative models and their use for robotics. The goal is for students to get both a high-level understanding of important problems in robot decision making as well as a low-level understanding of technical solutions. Course Website: https://16-831.github.io