Why this course

Robotics is moving quickly. Large-scale robot datasets, generative policies, vision-language-action models, and world models are changing what robot learning systems look like. The underlying problems, however, are remarkably persistent: a robot must still reason under uncertainty, infer state from incomplete observations, predict what happens next, choose actions over time, and learn from data and experience.

This course develops those foundations in detail. We study classical methods such as Bayesian filtering, Kalman filtering, dynamic programming, and optimal control not as historical artifacts, but as precise ways of thinking about fundamental robotics problems: estimation, prediction, and sequential decision-making. We then build toward imitation learning, reinforcement learning, model-based learning, offline learning, and the ideas that underpin modern robot-learning systems.

The goal is not to train you on one current architecture. It is to give you the mathematical depth to understand why modern methods work, where they fail, and how to reason about whatever comes next.

From foundations to modern robot learning

A recurring theme in the course is continuity. Many ideas that appear new in modern Physical AI have close relatives in classical robotics:

• State estimation → learned representations: what information about the world should a robot maintain?
• Bayesian filtering → uncertainty-aware learning: how should observations and prior knowledge be combined?
• Dynamics and control → world models: how should a robot predict the consequences of its actions?
• Dynamic programming and MPC → learned planning: how should a robot reason over future actions?
• Imitation and reinforcement learning → robot foundation models: how can behavior be learned and transferred from data?

These topics are a bridge to a future Learning in Robotics II course that will be devoted to modern robotics and foundation-model approaches.

Schedule: Spring 2027 — time and location TBA

Prerequisites

Required

• Proficiency in programming. All assignments will be based on Python but if you have used a similar language like MATLAB before, you should be able to pick up Python easily.
• Probability
• Linear Algebra

Self Assessment: Attempt Homework 0. This homework doesn’t require submission. If you find the questions manageable, you possess the necessary mathematical prerequisites for the course. If you think a brief review would enable you to tackle these questions, that will be our starting point for the course but the review will be fast. However, if these questions are entirely perplexing to you, it’s possible that you haven’t met the prerequisite requirements.

While primarily for graduate students, undergraduates may enroll with the instructor’s approval.

Recommended

• Machine Learning or Data Analysis (CS 4774 or CS 6316 or Equivalent)
• (Soft recommendation) Optimization

Evaluation

• 4 homeworks - Equally weighted
• No mid-term exam. No final exam.
• You will write a summary (it can be as elaborate as you like but at least 2 pages) that demonstrates your understanding of the material in your own words for each of the modules in the course. These summaries will together make up for 10% of your final grade. There is no partial credit here, depending on the quality of your summary, you either get all the 10% or none.

Academic Integrity

You are encouraged to collaborate with your peers for solving problems in the homework, reading books and curating other instructional materials to improve your understanding of the concepts taught in the class. While doing so, you might generate code/pseudo-code/solutions for the homeworks/project. When you begin to write your submission you should keep aside all these materials (including your friends) and do things “from scratch”. In short, everything you write/code and submit should be your own work done independently.

You should disclose all collaborations in your submission at the top. If you came across some code as a part of your homework/project you must mention it.

Collaboration is different from cheating. The latter will have serious consequences. Cheating is defined as attempting, abetting or using unauthorized assistance (knowledgeable friend who is not taking the class) or material (e.g., online code). Some examples of cheating are: copying someone else’s work for homework/exams, handing in someone else’s work as your own or handing in stuff from the Internet as your own work. These will not be tolerated. Your score for that particular homework or exam will be zeroed out if found guilty, you will be penalized one letter grade and this incident will be reported to the university.

I trust every student in this course to fully comply with all of the provisions of the University’s Honor Code. By enrolling in this course, you have agreed to abide by and uphold the Honor System of the University of Virginia, as well as the following policies specific to this course. All suspected violations will be forwarded to the Honor Committee, and you may, at my discretion, receive an immediate zero on that assignment regardless of any action taken by the Honor Committee. Please let me know if you have any questions regarding the course Honor policy. If you believe you may have committed an Honor Offense, you may wish to file a Conscientious Retraction by calling the Honor Offices at (434) 924-7602. For your retraction to be considered valid, it must, among other things, be filed with the Honor Committee before you are aware that the act in question has come under suspicion by anyone. More information can be found at http://honor.virginia.edu Your Honor representatives can be found at: http://honor.virginia.edu/representatives. Additionally, [Support Officer, if any enrolled], an Honor support officer enrolled in this class, is also available for questions.

Inclusive Environment

It is my goal to create a learning experience that is as accessible as possible. If you anticipate any issues related to the format, materials, or requirements of this course, please meet with me outside of class so we can explore potential options. Students with disabilities may also wish to work with the Student Disability Access Center to discuss a range of options to removing barriers in this course, including official accommodations. Please visit their website for information on this process and to apply for services online: sdac.studenthealth.virginia.edu. If you have already been approved for accommodations through SDAC, please send me your accommodation letter and meet with me so we can develop an implementation plan together.