Course Syllabus

CS 498: Robotics Team Project

Spring 2026

 

Class: 5-6:15pm, Tuesday and Thursday, Room: 0216 Siebel Center for Computer Science

Robotics Teaching Lab: Rooms 1105 and/or 1131, Siebel Center for Computer Science

 

 

1. Instructors

Prof.  Nancy M. Amato  

 

Dr. Irving Solis             

 

2. Course Description
This course consists of robotics team projects carried out in simulation and on physical robots, with project tracks such as F1TENTH autonomous driving and RoboCup 2D/3D soccer simulation. It is primarily a project-based course, with short, focused weekly lectures that provide guidance for the projects, while most of the time is spent in labs and hands-on implementation¹. Students work in teams to design, implement, and integrate algorithms for perception, localization, motion planning, and control into a complete robotic system that addresses a well-defined robotics challenge drawn from real-world or competition-inspired scenarios. Through hands-on laboratory work, simulation-based testing, and milestone-driven project progress, students gain strong technical competencies in robotic systems integration, as well as strategic thinking, communication, and teamwork skills essential for complex robotics projects. Evaluation is based on the successful demonstration of a fully working autonomous robotic system and on a written report and presentation describing the project design, implementation, and experimental results.

NOTE: Students registered for 4 credit hours will complete an additional activity related to the course topics, to be discussed with the instructor.

¹ Additional resources (readings, tutorials, and references) are provided to support the theoretical content.

 

3. Textbook: There is no required textbook for this course. All course materials will be provided by the instructor.

4. Course Motivation

Robots are complex systems composed of tightly coupled hardware and software components that must work together to accomplish a given task. To manage this complexity, a variety of system architectures have been proposed, yet regardless of the specific design, most robotic systems rely on a common set of core capabilities, including perception, localization and state estimation, task and motion planning, and control. Modern robotics development emphasizes modularity, reuse, and integration of these components rather than building monolithic solutions from scratch.

The motivation of this course is to prepare students to integrate modular robotics software and algorithms into a complete, working system that addresses a well-defined mobility challenge. Using tools and frameworks widely adopted by the robotics community, students will learn how individual subsystems interact, what fundamental problems they solve, and how their design choices affect overall system performance. Through hands-on projects in simulation and on physical robots, students will gain practical experience developing, testing, and refining integrated robotic systems applicable to real-world and competition-inspired scenarios such as F1TENTH autonomous driving and RoboCup 3D soccer simulation.

 

5. Learning Outcomes

After completing this course, students will be able to:

• Design and integrate a complete robotic system by implementing modular software components for perception, localization, motion planning, and control.
• Develop, test, and deploy robotics software in simulation and on physical platforms using modern robotics frameworks and visualization tools.
• Evaluate and debug robotic systems through systematic use of simulation, data logging, replay, and quantitative performance metrics.
• Iteratively improve system performance and robustness by making informed architectural and algorithmic decisions based on experimental results.
• Collaborate effectively in team-based projects, managing task allocation, development milestones, and technical communication.
• Demonstrate a fully functional robotic system that successfully addresses a well-defined robotics task.

By the end of the course, students will have a strong foundation in system-level robotics integration, preparing them for advanced coursework and complex robotics projects.

 

6. Prerequisites

Students taking this course are expected to have a solid foundation in programming, equivalent to CS 124 or CS 101, including the ability to write, read, and debug nontrivial programs. Familiarity with fundamental data structures and algorithmic thinking, as covered in CS 225, is strongly recommended, as students will work with structured data, modular codebases, and performance-aware implementations. A working knowledge of calculus and linear algebra is preferred, as these mathematical tools are commonly used to model system behavior, reason about transformations, and understand the underlying principles of many computational methods encountered throughout the course.

No prior robotics or hardware experience is required; undergraduate CS, Blended CS, and engineering students, as well as graduate CS and MEng Robotics & Autonomy students, are encouraged to enroll.

7. Course Structure and Topics

This is a project course and students will spend the majority of their time working in teams on their project. There will be short lectures, concentrated mostly in the beginning of the semester, providing high-level overviews of robotics topics that students will need during the course. These will serve as a review for students already familiar with these topics, and as introductions for other students, with pointers to additional materials for students who want to go deeper. There will be some tutorials and labs, also focused in the beginning of the semester, to help students get experience with some of the tools and techniques they will use on their projects. 

Topics covered in lecture will include:

• Robotics Foundations: Rigid-Body Transforms, Kinematics, Articulated Robotics
• Robotics System Architectures: Single vs. Multi-Agent Systems
• Control and Reactive Methods
• Perception
• Localization & Mapping
• Motion Planning 
• Optimization

 

8. Grading

40% — Labs and Assignments: Hands-on laboratory exercises supporting key concepts in perception, planning, control, and system integration.

10% — Quizzes (Individual): Short quizzes assessing understanding of core concepts and material covered in lectures and tutorials.

30% — Demonstrations (Group): Implementation and integration of either an F1TENTH autonomous vehicle or a RoboCup 3D soccer simulation team. Evaluation is based on two to three periodic project demonstrations during the semester and a final demonstration.

10% — Final Project Report and Code Submission (Group): A written report and accompanying code documenting the final project, including system architecture, algorithms, implementation details, and experimental results, with an emphasis on clarity and reproducibility.

10% — Peer and Course Staff Evaluation: Anonymous evaluation of individual contributions within the team, including collaboration, technical involvement, and overall participation, assessed by teammates and teaching staff.

Grading Scale

+90 = A

80-90 = B

70-80 = C

60-70 = D

-60 = E

Late Submission Policy: Late submissions will be accepted but will be subject to a grade penalty.

 

9. Syllabus Statements

Attendance Policy

Students are expected to attend all scheduled classes, labs, and project activities. Attendance is required for labs, discussions, and project demonstrations. While attendance is not directly graded, quizzes will be conducted during class sessions, and missing class without prior notice will result in loss of quiz points. Students are expected to notify the instructors in advance when they will miss a class whenever possible; absences without prior notice will generally only be excused in cases of illness or other emergencies. Students are responsible for all course material covered during any absence.

Academic Integrity and the use of AI Tools

Generative AI, such as ChatGPT, Microsoft Copilot, and Gemini, can answer questions and generate text, code, images, and other media. The appropriate use of generative AI varies from course to course. In this course, there are times when generative AI may be useful in supporting learning and development. If you choose to use generative AI as permitted below, you must document and attribute all AI contributions to your coursework and take full responsibility for those contributions, including correctness, understanding, and reliability. All assignments in this course must include an AI Disclosure section describing whether AI was used, which tools were used, how they were used, and the extent of their use. When using generative AI, you are expected to keep a brief record of prompts and outputs, which instructors may request.

You may use generative AI in this course for brainstorming ideas, understanding concepts and documentation, debugging assistance, code explanation, refactoring suggestions, and improving clarity or organization of written or technical material.

You may NOT use generative AI in this course to generate complete solutions, core project implementations, or competition code without substantial student contribution and understanding; during exams, in-class evaluations, or project demonstrations, unless explicitly permitted; or by uploading private assignment or project code to external AI services.

If you have questions about the use of generative AI, please contact the instructors. Failure to follow these guidelines constitutes a violation of academic integrity and will be handled in accordance with the University of Illinois Student Code.

The University of Illinois at Urbana-Champaign Student Code is considered part of this syllabus. Students should review Article 1, Part 4: Academic Integrity, available at http://studentcode.illinois.edu/. Academic dishonesty may result in a failing grade. Students are responsible for understanding and complying with the Academic Integrity Policy (https://studentcode.illinois.edu/article1/part4/1-401/); lack of awareness is not an excuse. Students are encouraged to consult the instructors if they are unsure about what constitutes plagiarism, cheating, or other violations of academic integrity.

Learning Environment 

The intent of this section is to raise student, course staff, and instructor awareness of the need to take personal responsibility in creating an effective and respectful learning environment in this course, and generally in our campus community, and to provide pointers to relevant school and campus resources. 

All members of the Siebel School of Computing and Data Science - faculty, staff, and students - are expected to adhere to the School's Values and Code of Conduct. The CS CARES Committee is available to serve as a resource to help people who are concerned about or experience a potential violation of the Code. If you experience such issues, please contact the CS CARES Committee. The Instructor(s) of this course are also available for issues related to this class.

Behavior that persistently or grossly interferes with course activities is considered disruptive behavior and may be subject to disciplinary action. Such behavior inhibits other students’ ability to learn and an instructor’s ability to teach. A student responsible for disruptive behavior may be required to leave class pending discussion and resolution of the problem and may be reported to the Office for Student Conflict Resolution ( https://conflictresolution.illinois.edu; conflictresolution@illinois.edu; 217-333-3680) for disciplinary action.

As members of the Illinois community, we each have a responsibility to express care and concern for one another. If you come across a classmate whose behavior concerns you, whether in regard to their well-being or yours, we encourage you to refer this behavior to the Connie Frank CARE Center (formerly the Student Assistance Center) in the Office of the Dean of Students.  You may do so by calling 217-333-0050 or by submitting an online referral.  Based on your report, staff in the Student Assistance Center will reach out to offer support and assistance. 

Further, as a Community of Care, we want to support you in your overall wellness. We know that students sometimes face challenges that can impact academic performance (examples include mental health concerns, food insecurity, homelessness, personal emergencies). Should you find that you are managing such a challenge and that it is interfering with your coursework, you are encouraged to contact the Connie Frank CARE Center (formerly the Student Assistance Center) in the Office of the Dean of Students for support and referrals to campus and/or community resources.

Emergency Response Information

Emergency response recommendations and campus building floor plans can be found at the following website: https://police.illinois.edu/em/run-hide-fight/. I encourage you to review this website within the first 10 days of class.

Disability-Related Accommodations

The University of Illinois is committed to ensuring that all students, including those with disabilities, do not experience barriers to learning and participating fully in class. If you have a letter of accommodation from DRES and have not already given it to the instructor, please do so as soon as possible to ensure your accommodation needs are met. 

To obtain disability-related academic adjustments and/or auxiliary aids, students with disabilities must contact Disability Resources and Educational Services (DRES) as soon as possible. To contact DRES, you may visit 1207 S. Oak St., Champaign, call 333-1970, email: disability@illinois.edu, or go to the DRES website.

Religious Observances

It is the policy of the University of Illinois Urbana-Champaign to reasonably accommodate its students’ religious beliefs, observances, and practices that conflict with a student’s class attendance or participation in a scheduled examination or work requirement, consistent with state and federal law. 

Students should examine this syllabus at the beginning of the semester for potential conflicts between course deadlines and any of your religious observances. If a conflict exists, you should  make requests for accommodation in advance of the conflict to allow time for both consideration of the request and alternate procedures to be prepared.  Requests should be directed to the instructor.  The Office of the Dean of Students provides an optional resource on its website to assist students in making such requests.

Statement on Mental Health

Significant stress, mood changes, excessive worry, substance/alcohol misuse or interferences in eating or sleep can have an impact on academic performance, social development, and emotional wellbeing. The University of Illinois offers a variety of confidential services including individual and group counseling, crisis intervention, psychiatric services, and specialized screenings which are covered through the Student Health Fee. If you or someone you know experiences any of the above mental health concerns, it is strongly encouraged to contact or visit any of the University’s resources provided below.  Getting help is a smart and courageous thing to do for yourself and for those who care about you.

• Counseling Center (217) 333-3704

• McKinley Health Center (217) 333-2700

• National Suicide Prevention Lifeline (800) 273-8255

• Rosecrance Crisis Line (217) 359-4141 (available 24/7, 365 days a year)

• Additional resources available on the SSCDS Student Resources Page

If you are in immediate danger, call 911.

Sexual Misconduct Reporting Obligation

The University of Illinois is committed to combating sex-based misconduct. Faculty and staff members are required to report any instances of sex-based misconduct to the University’s Title IX Office. In turn, an individual with the Title IX Office will provide information about rights and options, including accommodations, support services, the campus disciplinary process, and law enforcement options.

A list of the designated University employees who, as counselors, confidential advisors, and medical professionals, do not have this reporting responsibility and can maintain confidentiality, can be found here: wecare.illinois.edu/resources/students/#confidential.

Other information about resources and reporting is available here: wecare.illinois.edu.

Family Educational Rights and Privacy Act (FERPA)

Any student who has suppressed their directory information pursuant to Family Educational Rights and Privacy Act (FERPA) should self-identify to the instructor to ensure protection of the privacy of their attendance in this course. See https://registrar.illinois.edu/academic-records/ferpa/ for more information on FERPA.