This course provides a practical introduction to training robots using data-driven methods. Key topics include data collection methods for robotics, policy training methods, and using simulated environments for robot learning. Throughout the course, students will have hands-on experience to design simulation environments, collect data, train policies, and evaluate policies. A solid working knowledge of Python and a basic understanding of machine learning are prerequisites. The course focuses entirely on the project, with no additional assignments.
Register NowPeople say robotics is all about data, but how do we collect it? Learn and experience how the community is collecting data for robots.
With the data collected, how do we train a policy to perform the task autonomusly? Learn various policy training methods commonly used in the community.
What role does simulation play in robotics? Learn how to model your environment in simulation and transfer your learned policy to the real world.
Classes held in Stata 32-124 / Office hours held in 45-322
Robot Learning Basics — Manipulation Focused
After the lectures, you'll have the opportunity to apply what you've learned through hands-on experimentation. The remainder of the IAP course following January 17 will consist of office hours and independent project work. We will announce the detailed office hours schedule soon.
Gather robot manipulation data through teleoperation with virtual reality interfaces like Apple Vision Pro.
Train robot policies using behavioral cloning and reinforcement learning techniques
Test and evaluate trained policies in realistic simulation environments
Optional opportunity to deploy trained policies on real robot hardware
MIT CSAIL, Post-Doc
MIT CSAIL, PhD Student
MIT CSAIL, MEng Student
MIT CSAIL, Visiting Researcher
MIT CSAIL