This repository is the course website of CS391R: Robot Learning (Fall 2020) at UT-Austin.
Robots and autonomous systems have been playing a significant role in the modern economy. Custom-built robots have remarkably improved productivity, operational safety, and product quality. However, these robots are usually programmed for specific tasks in well-controlled environments, unable to perform diverse tasks in the real world. How can we take robots out of constrained environments to our daily life, to assist us in a variety of real-world tasks as our companion and assistant? It demands a new form of general-purpose robot autonomy that robots understand the world through the lens of its perception and make informed decisions accordingly. This course studies modern machine learning and AI algorithms for autonomous robots as an embodied intelligent agent. It covers advanced topics that center around the principles and techniques on 1) how robots perceive the unstructured environments from raw sensory data, 2) how robots make decisions based upon its perception, and 3) how robots learn and adapt actively and continually in the physical world.
This class is intended for graduate students and ambitious undergraduates who are passionate about the emerging technologies at the intersection of Robotics and AI, especially for those who seek research opportunities in this subject area. Through this course, students will:
- understand the potentials and societal impacts of general-purpose robot autonomy in the real world, the technical challenges arising from building it, and the role of machine learning and AI in addressing these challenges;
- get familiar with a variety of model-driven and data-driven principles and algorithms on robot perception and decision making;
- be able to evaluate, communicate, and apply advanced AI-based techniques to problems in robotics.