Course Syllabus

CS 498: Robotics Team Project

Fall 2026

 

Class: 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. Couse Staff

Instructors: 

Professor Nancy M. Amato, Abel Bliss Professor of Engineering and School Director, Siebel School of Computing and Data Science

Dr. Irving Solis, UIUC CS Future Faculty Fellow, Siebel School of Computing and Data Science

TAs: TBA

CAs: TBA

 

2. Course Description
This project-based course focuses on robotics team projects carried out in simulation and on physical robotic platforms, with tracks based on the F1TENTH autonomous driving car and the Booster K1 humanoid robot. Students will work in teams to design, implement, and demonstrate a robotics project using one of these platforms, which may involve reproducing or extending a research paper, developing a real-world robotics application, or exploring a creative robotic behavior. Short lectures and assignments will support the final project by introducing the full robotics pipeline, including perception, localization, planning, and control, with topics ranging from classical algorithms to advanced learning-based methods. By working in teams, students will also develop important professional skills that naturally arise in collaborative robotics projects, including communication, coordination, shared problem-solving, and strategic decision-making. Evaluation is based on assignments, project milestones, the final robot demonstration, and a written report and presentation. 

NOTE: Students registered for 4 credit hours will complete additional activities that extend both the assignments and the final project, 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

Robotics is a highly interdisciplinary and complex field that brings together several tightly connected areas, including perception, localization and state estimation, planning, and control. Although each of these areas can be studied independently, developing a strong understanding of robotics often requires seeing how they interact within a complete system. In practice, it can be difficult for students to gain experience with the full robotics pipeline, since many courses focus on individual components rather than on how those components are integrated, tested, and improved together. This course is motivated by the need to give students the opportunity to work across the full pipeline and understand how decisions made in one module affect the behavior and performance of the entire robotic system.

To support this goal, the course is designed around a final project in which students implement a robotics system that integrates multiple core components of the robotics pipeline, including perception, planning, and control. Lectures and assignments provide the foundational knowledge needed to understand these components in more detail and to apply them effectively in the final project. The robotic platforms used in the course, including the F1TENTH autonomous driving car and the Booster humanoid robot, are representative of platforms used in competition-inspired and research-oriented robotics challenges. This allows students to begin with a basic working system and progressively improve it by replacing, extending, or refining individual modules. By following a modular approach, students can analyze the robotic system as a whole while also studying each component separately, gaining practical insight into how modern robotic systems are built, integrated, and improved.

 

5. Learning Outcomes

After completing this course, students will be able to:

  • Explain the main components of the robotics pipeline, including perception, localization and state estimation, planning, and control, and describe how they interact within a complete robotic system.
  • Apply track-specific robotics technologies, including autonomous navigation and software frameworks such as ROS, or humanoid robot planning, Isaac Sim, and AI model training, depending on the selected track.
  • Develop a complete robotics project from problem formulation and system design through implementation, testing, evaluation, and deployment, applying classical robotics algorithms and learning-based methods in simulation or on physical robotic platforms.
  • Analyze, debug, evaluate, and iteratively improve individual modules and the overall robotic system using experimental results, visualization tools, quantitative performance metrics, and informed architectural and algorithmic decisions.
  • Collaborate effectively in team-based robotics projects and clearly communicate the project design, implementation, evaluation, and results through code, documentation, written reports, demonstrations, slides, and oral presentations.

By the end of the course, students will have practical experience in system-level robotics development and will be prepared for advanced coursework, research, competitions, 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-based course, and students will spend most of the semester working in teams on the design, implementation, testing, and evaluation of a robotics project. Short lectures, concentrated primarily at the beginning of the semester, will provide high-level overviews of the robotics concepts needed for the course. These lectures will serve as a review for students who are already familiar with the material and as an introduction for others, with additional resources provided for further study. Tutorials and introductory assignments will also help students become familiar with the software, simulation environments, hardware, and development tools used in their selected track.

Topics covered in lectures may include:

    • Robotics foundations: rigid-body transformations, kinematics, and articulated robotics
    • Robotics system architectures and single- and multi-robot systems
    • Control and reactive methods
    • Perception
    • Localization and mapping
    • Motion planning
    • Optimization
    • Learning-based methods for robotics

NOTE: Course activities and assignments will vary depending on the selected project track.

    • In the autonomous-driving track, students will implement and evaluate foundational perception, planning, and control methods within a provided software repository. Assignments will emphasize implementation, simulation-based verification, debugging, and parameter tuning. Some assignments may also involve deploying and tuning software on a physical F1TENTH vehicle to verify that the complete system operates correctly.

The autonomous-driving track will also include periodic races or system demonstrations throughout the semester, followed by a final competition. Participation in these activities may occasionally require students to be available outside regular class hours, including on weekends.

    • In the humanoid-robot track, students will work with humanoid planning, Isaac Sim, and learning-based robotics methods. Assignments may require students to configure, train, and evaluate AI models, monitor longer-running training processes, and verify that the resulting behaviors perform reliably in simulation before they are integrated into the final project.

Across all tracks, students will progressively apply the concepts and tools introduced in lectures and assignments to a complete team project.

8. Grading

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

10% — Quizzes: Short quizzes assessing students’ understanding of core concepts and material covered in lectures, tutorials, and assignments.

40% — Final Project: Evaluation of the team’s final robotics project, including the quality of the implementation, performance in simulation and on the physical robot, and the supporting materials such as the final report, documentation, slides, and code submission.

10% — Peer and Course Staff Evaluation: Assessment of individual contributions within the team, including collaboration, technical involvement, participation, and overall contribution to the project.

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.