Robot Learning from Demonstration and Interaction (CS309)
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pizza
utexas/spring2021/52140 -
github
fri-robotlearning-spring2021 -
github classroom
fri-robotlearning-spring2021 -
ut canvas
Robot Learning FRI
ut canvasCurrently, 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.
There are six types of assignments that the students are expected to complete.
The overall grade will consist of five of the coursework assignments.
Grades will be assigned using both plus and minus grades.
The schedule is subject to change due to pace of class, see website for updates.
Assignment dates will be announced shortly!
| Day | Topic | Readings | Assignments |
|---|---|---|---|
| Tue 01/19 | Motivation, Organization | ||
| Thu 01/21 | Workspace Setup | ||
| Tue 01/26 | Python Introduction | ||
| Thu 01/28 | Linear Algebra Refresher | ||
| Tue 02/02 | Regression: OLS, Ridge Regression | ||
| Thu 02/04 | Regression: Lasso, Elastic Nets | ||
| Tue 02/09 | Regression: Features, Polynomial Regression | ||
| Tue 02/11 | Robotics: basics, position, orientation | ||
| Tue 02/16 | Robotics: homogeneous transformation | ||
| Thu 02/18 | Robotics: Inverse Kinematics, Singularities, Dynamics | ||
| Tue 02/23 | Teaching Types: Kinesthetic, Tele-Operation, Observation Designing (Collaborative) Tasks: goals, requirements, challenges |
1. Introduction 2. Design choices |
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| Thu 02/25 | Trajectory Representation | ||
| Tue 03/02 | Classification: FLD, Perceptron | ||
| Thu 03/04 | Classification: SVM | ||
| Tue 03/09 | Clustering: KDE, KNN | ||
| Thu 03/11 | Clustering: Mean-Shift, K-means | ||
| Tue 03/23 | Evaluation: Model Selection, Cross Validation | ||
| Thu 03/25 | Reinforcement Learning: MDP, Features | ||
| Tue 03/30 | Pytorch Introduction | ||
| Thu 04/01 | Pytorch Continued | ||
| Tue 04/06 | Reinforcement Learning: Bellman Optimality, Exploration vs Exploitation | ||
| Thu 04/08 | Imitation Learning: Behavioral Cloning | 4. Deriving a policy: The source of the state to action mapping |
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| Tue 04/13 | Imitation Learning: Inverse Reinforcement Learning | ||
| Thu 04/15 | Imitation Learning: Inverse Reinforcement Learning | ||
| Tue 04/20 | Project Meetings | Final Project | |
| Thu 04/22 | Project Meetings | ||
| Tue 04/27 | Project Meetings | ||
| Thu 04/29 | Project Meetings | ||
| Tue 05/04 | Project Meetings | ||
| Thu 05/06 | Project Meetings | 5. Limitations of the demonstration dataset 6. Future directions 7. Conclusion |
|
| Tue 05/14 (subject to change) |
Final Project Presentations |
Several office hours are offered by various mentors. The office hours are held virtually via zoom. Always show up at the beginning of the office. The mentors are instructed to end the office hours if there are no students in the first 20 minutes.
| Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|
| 09:30 | |||||
| 10:00 | Aditi | Aditi | |||
| 10:30 | |||||
| 11:00 | Sydney | Steve | Sydney | ||
| 11:30 | |||||
| 12:00 | |||||
| 12:30 | |||||
| 13:00 | |||||
| 13:30 | Ilham | Yian | |||
| 14:00 | Zachary | ||||
| 14:30 | |||||
| 15:00 | Ilham | ||||
| 15:30 | Lecture | Lecture | |||
| 16:00 | Zachary | ||||
| 16:30 | |||||
| 17:00 | Yian | Steve | |||
| 17:30 | |||||
| 18:00 |
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.