Course Logistics
Course Format#
The course will be a mix of lectures and paper discussions. Students will be given a large list of papers (both new and old) related to the weekly topics as readings, and will have to sign up to present a paper or scribe once in the semester. Weekly lectures will be broken down in the following way: Mondays will be dedicated to the instructor for presenting a high-level area in robot learning, and Wednesday will have two students presenting chosen papers on the weekly topic, as well as a group discussion. Students are heavily encouraged to read all the papers and are expected to actively participate in the group discussion. There will be two mandatory papers for students to read for every paper discussion, and students will write a 3 paragraph "paper review" quickly summarizing and discussing these two papers. Finally, in order for students to explore a hands-on area, there will be an open-ended homework and class project.
Prerequisites#
- Working knowledge of deep learning, reinforcement learning, computer vision and robotics is assumed.
- Experience with reading and understanding academic papers.
- This is NOT a basic course in reinforcement learning, computer vision, robotics, deep learning or AI. This course is meant for you to engage in an interdisciplinary discussion and think about embodied intelligence from different perspectives.
Course Information#
Class Time and Location#
Time: Monday/Wednesday 11:50am-1:10pm
Location: NSH 1305
Office Hours#
| Office Hours | |
|---|---|
| Deepak Pathak | Wednesday 1:10-2:00 PM, start at NSH 1305, move to EDSH 218 |
| Alex Li | Monday 1:10-2:00 PM, EDSH 220 |
Class Forum#
Forums are on Piazza (sign up with your andrew email address). Please check out the Piazza regularly, as we will make new announcements on Piazza. We encourage you to engage in discussions on Piazza as well.
Grading Policy#
| Percentage | |
|---|---|
| Paper Presentation and Scribing | 20% |
| Paper Reviews | 20% |
| Homework | 15% |
| Final Project | 45% |
Academic Integrity#
Collaboration is encouraged, but the work you submit for assignments is expected to be entirely your own. That is, the writing and code must be yours, and you must fully understand everything that you submit. Discussing a paper or the details of how to solve a problem is fine, but you must write your submission yourself. If we find highly identical work without proper accreditation of collaborators, we will take action according to university policies. For more, see the CMU academic integrity guidelines.