CS378: Robot Learning from Demonstration and Interaction

Course Info

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 real 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 in PHR 2.114 on Tuesdays and Thursdays from 15:30 to 17:00 if not announced otherwise. Attendance is mandatory. Students are expected to email the instructor in advance to inform of any potential absences.
The practical parts of the lecture will take place in the FRI Lab in GDC 5.710A.

Readings: There is no textbook for this course. However, the following readings and online tutorials will be beneficial at different stages of the research projects.

• Asurveyofrobotlearningfromdemonstratio, Argall et al.
• ROS Tutorials
• Sawyer Gazebo Tutorial
• Using Probabilistic Movement Primitives in Robotics, Paraschos et al.
• PyTorch 60min Blitz

Prerequisites: FRI Robot Learning Spring 2019 (50670)

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

Planned Lecture Schedule

Grading

Overall grades will be determined from:

• Class participation and atendance: 15%
• Final Report and Presentation: 85%

Grades will be assigned using both plus and minus grades.

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.