Logistics

Grading Policy

Paper reviews (30%)
Student presentation (20%)
Course project (40%)
In-class participation (10%)

Student Presentations

An integral component of this course is to conduct a systematic literature review on robot learning research through student presentations and in-class discussions. Each student should expect to have at least one presentation regarding their assigned paper readings. To ensure the quality and clarity of the presentations, we expect the students to
  • read the assigned papers thoroughly and gain a good understanding before making the presentation slides.
    Template: [Powerpoint] or [Google Slides]
  • email the slides to the TA and the instructor seven days (EOD) prior to the presentation date for feedback and revision.
    Deadline: 9:59pm Tue for talks next Tue and 9:59pm Thu for talks next Thu
Failures to email the slides on time would incur a 20% deduction on the presentation score. Each presentation should be 20min (± 2min). The presentations will be graded in the following aspects:
  • Clarity of presentation (problem formulation, proposed method, key results);
  • Review of prior work and the challenges addressed by this work;
  • Analysis of the strengths and weaknesses of the research;
  • Discussion of potential research extensions and applications;
  • Response to student questions (in-class and on Canvas).
After each presentation, we will do a 5min Q&A about the presentation. Two papers will be presented in each class, and then we will have a 20min open-ended discussion. The discussion questions will be posted on Canvas by the instructor the day before the class. The slides and video recordings of the presentations will be shared on Canvas within one week of the presentation date. The presentation will be worth 20% of the total grade. For students who present more than once (based on availability), the final presentation grade will be the highest score of their presentations.

Useful Resources

Paper Reviews

A paper review will be due by 9:59pm the previous night for each class of student presentations (9:59pm Mon and 9:59pm Wed). There will be two papers presented in each class. You need to pick one paper between them to write a review. The paper reviews are in a similar review format as the RSS Conference. The reviews are graded based on the level of understanding and critical analysis of the work. Please submit the reviews using the online form: [Review Form]

The reviews will be each worth 1.5% of the total grade and 20 reviews are required, summing up to 30% of the total grade. The reviews will not be accepted late since we expect the students' independent evaluations of the papers prior to in-class presentations and discussions. However, there are 22 presentation classes. It allows flexibility for students to skip some reviews due to personal conflicts, such as holy days, conference travel, etc. In the case when more than 20 reviews are submitted by a student, we will take the top-scored 20 for grading. Note that class attendance and participation are required in order to receive a non-zero grade of paper review. For students studying online in a different time zone, they may submit their written responses to the discussion questions on Canvas within 48hrs after the class as an alternative to in-class attendance.

Useful Resources

Course Project

The course project aims to help the students gain in-depth, hands-on experiences applying AI-based techniques to practical robot perception and decision making problems. It consists of three key milestones: a project proposal (5%), a milestone report (5%), a final report (25%), and a spotlight talk (5%). For more detail, please see the Project page.

Useful Resources

COVID-19 Updates

Safety Information

While we will post information related to the contemporary situation on campus, you are encouraged to stay up-to-date on the latest news as related to the student experience. Check out this website for more information.

"Keep Learning" Resources

This course may be offered in a format to which you are unaccustomed. If you are looking for ideas and strategies to help you feel more comfortable participating in our class, please explore the resources available here.

Sharing of Course Materials is Prohibited

No materials used in this class, including, but not limited to, lecture hand-outs, videos, assessments (quizzes, exams, papers, projects, homework assignments), in-class materials, review sheets, and additional problem sets, may be shared online or with anyone outside of the class unless you have my explicit, written permission. Unauthorized sharing of materials promotes cheating. It is a violation of the University’s Student Honor Code and an act of academic dishonesty. I am well aware of the sites used for sharing materials, and any materials found online that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in sanctions, including failure in the course.

Class Recordings

Class recordings are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings.

COVID Caveats

To help keep everyone at UT and in our community safe, it is critical that students report COVID-19 symptoms and testing, regardless of test results, to University Health Services, and faculty and staff report to the HealthPoint Occupational Health Program (OHP) as soon as possible. Please see this link to understand what needs to be reported. In addition, to help understand what to do if a fellow student in the class (or the instructor or TA) tests positive for COVID, see this University Health Services link.

Academic Integrity

You are encouraged to discuss assignments with classmates, but all collected data, analysis, images and graphs, and other written work must be your own. All programming assignments must be entirely your own except for teamwork on the final project. You may NOT look online for existing implementations of algorithms related to the programming assignments, even as a reference. Your code will be analyzed by automatic tools that detect plagiarism to ensure that it is original. For the final project, you have full access to the web, but all ideas, quotes, and code fragments that originate from elsewhere must be cited according to standard academic practice. Students caught cheating will automatically fail the course and will be reported to the university. If in doubt about the ethics of any particular action, look at the departmental guidelines and/or ask — ignorance of the rules will not shield you from potential consequences.

Notice about students with disabilities

The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. For more information, contact the Division of Diversity and Community Engagement — Services for Students with Disabilities at 512-471-6529; 512-471-4641 TTY.

Notice about missed work due to religious holy days

A student who misses an examination, work assignment, or other projects due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to satisfactorily complete the missed assignment or examination within a reasonable time after the excused absence.