Robot Learning (FRI I)

Spring 2025
 

Course Description

Currently, most robots are programmed by experts in a controlled setting. For robots to do meaningful work in human environments, we must develop autonomous systems capable of learning from their experience, and learning from people. Robot learning research seeks to bring to bear modern machine learning and artificial intelligence algorithms on problems in robotics, integrating learning with classical techniques in perception, planning, and control. This course will provide an overview of fundamental robotics and machine learning concepts and survey modern robot learning research.

The course is part of an effort to build a community of undergraduate researchers in the Robot Interactive Intelligence (RobIn) Lab directed by Professor Roberto Martín-Martín.

Course Time and Location
Time: Tue/Thu 3:30 to 5pm
Location: WEL 3.310

Online Platforms
Ed Discussion
Canvas
Gradescope

Instructor

Ben Abbatematteo
OH: Mon 4:30pm, Booking Link
Office: GDC 3.430
Email: abba [at] cs.utexas.edu

Peer Mentors

Luca Macesanu
luca.m [at] utexas.edu
OH: Tues 10am-12pm
Location: AHG 1.220
Tarun Kholay
tarunkholay [at] utexas.edu
OH: Mon 10am-12pm
Location: AHG 1.220
Boueny Folefack
bouenyf [at] utexas.edu
OH: TTh 12:30 - 1:30pm
Location: AHG 1.220
Arnav Balaji
arnavbalaji21 [at] utexas.edu
OH: Mon 3:30-5pm, Tues 5-6pm
Location: AHG 1.220
Sriniket Ambatiputi
asriniket [at] utexas.edu
OH: TTh 2-3pm
Location: AHG 1.220

Learning Objective

As part of the Freshman Research Initiative, this course aims to provide students with an introductory experience to robotics research. Through this course, students will:

  • Learn basic elements of robotics like rigid body kinematics and motion planning.
  • Gain a basic understanding of machine learning from linear regression to Transformers.
  • Apply these concepts in projects spanning the modern robotics stack.

Credits

This course is presented by Ben Abbatematteo in conjunction with Professor Roberto Martín-Martín as part of the Freshman Research Initiative. The course takes inspiration from Justin Hart's Autonomous Robots FRI stream.