Robot Learning from Demonstration and Interaction (CS378)

  • semester
    spring 2020
  • time
    tue 15:30-17:00
    thu 15:30-17:00
  • lecture room
    Zoom
  • instructor
    Rudolf Lioutikov
  • e-mail

Course Description

Currently, most robots are programmed meticulously by robotics experts in a controlled laboratory setting—a model that is obviously not scalable to large-scale deployment. Robot learning from demonstration has emerged as an alternative paradigm for teaching robots new tasks by simply showing them what to do, rather than by writing code. Thus, learning from demonstration research tries to answer the question: “How can robots learn to interpret and generalize human demonstrations?” Solving this core research problem will enable the next generation of personal robots to revolutionize the home and workplace in coming years.

This research stream will place students at the cutting-edge of robot learning from demonstration research, working with robots to perform complex manipulation tasks, such as autonomously building IKEA furniture. Students will be given instruction in three core areas of robotics: manipulation, perception, and human-robot interaction. Additionally, students will learn and practice programming skills via hands-on mini-projects in each of these areas. After these key competencies have been acquired, students will devise and implement research projects in the area of learning from demonstration.

This course is the continuation of the FRI Robot Learning spring lecture. The students will expand and apply the knowledge gained in the spring semester in research projects chosen jointly by the student project teams and the instructor. Each project will require a general understanding of robotics and machine learning as well as detailed knowledge of the particular sub-field the project operates in. The research conducted within each project aligns with the research directions of the Personal Autonomous Robotics Lab and ideally leads to or supports a publication in a major robotics / machine learning conference. Students grow into productive researchers, working on robot platforms in an actual robot learning research group, presenting challenges, experiences and rewards that differ significantly from most undergraduate classes.

Organization

Class sessions will be held via Zoom on Tuesdays and Thursdays from 15:30 to 17:00 (CST) if not announced otherwise. Students are expected to email the instructor in advance to inform of any potential absences.

Prerequisites

Robot Learning from Demonstration and Interaction (CS309).
Intrinsic motivation and a healthy thirst for knowledge.

Programming Language

Course programming assignments will be in Python 3. We do not assume that students have previous experience with the language, but the students are expected to learn the basics very rapidly.

Coursework

At the beginning of the course, project ideas will be presented by the instructor. Those ideas are intended as a stating point for the students to derive their own research projects from. The teams can of course suggest their own projects. The instructor will help do define each project such that it balances ambition with pragmatism. The students and the instructor will commit to a project idea early in the semester and the instructor will guide and support the students with the project to a reasonable degree. However, the students are expected to work independently and in teams on these projects. A sucessfull participation in this course requires a positive evaluation in all of the following milestones:

  1. Project Proposal
  2. Intermediate Project Presentation
  3. Project Report
  4. Final Project Presentation

Grading

Overall grades will be determined from:

  • Project: 40%
  • Final Report: 25%
  • Review: 10%
  • Final Presentation: 25%
Grades will be assigned using both plus and minus grades.

Lecture Schedule

The schedule is subject to change due to pace of class, see website for updates.

Day Topic
Thu 08/27 Initial Project Ideas | Team Formation
Thu 09/03 Discuss Project Proposals
Thu 09/10 Project Proposal Presentation
Thu 10/22 Intermediate Project Presentation
Tue 12/01 Project Report due
Tue 12/08 Review due
Tue 12/15 Final Project Presentations

Academic Honesty Policy

You are encouraged to discuss the readings and concepts with classmates, but all written work must be your own. Programming assignments must be your own, except for 2-person teams when teams are authorized. 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.
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 project 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 complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.

Safety and In-Person Participation/Masks

The lecture will be completely online, but in case students volunteer to work with a real robot platform and the lab facilities allow for the student presence special precautions are required.

We will all need to make some adjustments in order to benefit from in-person classroom interactions in a safe and healthy manner. Our best protections against spreading COVID-19 on campus are masks (defined as cloth face coverings) and staying home if you are showing symptoms. Therefore, for the benefit of everyone, this is means that all students are required to follow these important rules.

  • Every student must wear a cloth face-covering properly in class and in all campus buildings at all times
     
  • Students are encouraged to participate in documented daily symptom screening.  This means that each class day in which on-campus activities occur, students must upload certification from the symptom tracking app and confirm that they completed their symptom screening for that day to Canvas.  Students should not upload the results of that screening, just the certificate that they completed it. If the symptom tracking app recommends that the student isolate rather than coming to class, then students must not return to class until cleared by a medical professional.
     
  • Information regarding safety protocols with and without symptoms can be found here.

If a student is not wearing a cloth face-covering properly in the classroom (or any UT building), that student must leave the classroom (and building). If the student refuses to wear a cloth face covering, class will be dismissed for the remainder of the period, and the student will be subject to disciplinary action as set forth in the university’s Institutional Rules/General Conduct 11-404(a)(3). Students who have a condition that precludes the wearing of a cloth face covering must follow the procedures for obtaining an accommodation working with Services for Students with Disabilities.

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.