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.
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:
Overall grades will be determined from:
The schedule is subject to change due to pace of class, see website for updates.
|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|
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.
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.
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.
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.
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.
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.
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.