UTK COSC 494/594: Robot Learning (Spring 2026)

Time and Location

• Lectures: M/W/F, 13:50 pm - 02:40 pm, in MKB-524

• Office Hours:

  • Fei Liu: W 3:00pm-4:00pm, MKB-612

    Farong Wang: TBD

Instructors

• Fei Liu: fliu33@utk.edu

• TA: Farong (Baylee) Wang: fwang31@vols.utk.edu

Course Description

Robots operate in uncertain, dynamic environments by sensing through noisy sensors, acting through imperfect actuators, and improving performance through experience. This course covers the fundamental principles underlying robot perception, planning, control, and learning. Topics span probabilistic state estimation, optimal control and planning, and modern reinforcement learning methods. The course emphasizes both theoretical foundations and hands-on implementation for robotic systems.

The students are expected to sign up on Canvas.

Course Syllabus: PDF

Prerequisites

The students are expected to have background in linear system theory, probability theory, and optimization theory, as well as a strong programming background.

Grading

Grading will be based on the following rubric.

• Participation (10%). Participation points can be earned by attending class, actively engaging in discussions during class and on Canvas.

• Project (70%). There will be 4 coding projects throughout the semester. Students are expected to complete all projects individually.

• Paper Reading & Presentation (70%). Each student are expected to read a paper related to the course, and deliver a in-class 10min presentation + 5min Q&A for the paper.

Late Submission Policy: All deadlines are firm unless notified in advance. Late submissions can only be accepted within next 24hr of the deadline but will result in a straight 25% off of grade.