This is INFO 6940-031: Robotics Seminar Special Topics Course focused on seminal robotics papers spanning perception, planning, and human-robot interaction (PhD-level, Master’s students are welcome). Lectures include student lectures and invited talks from guest speakers who are experts in the field, run in conjunction with the Cornell Robotics Seminar in Ithaca (remotely). Students are expected to give one presentation and organize a discussion session to invoke interesting conversation about a paper from top robotics/AI venues including RSS, ICRA, IROS, HRI, and Neurips that you find interesting.
Prof. Angelique Taylor
- Office hours: Monday from 11:30 AM-12:30 PM [Zoom Link] and in Bloomberg 262
- Email: email@example.com
This is a PhD course focused on seminal robotics papers spanning perception, planning, and human-robot interaction (Master’s students are welcome). Lectures include student lectures and invited talks from guest speakers who are experts in the field, run in conjunction with the Cornell Robotics Seminar in Ithaca (remotely). Students are expected to give one presentation and organize a discussion session to invoke interesting conversation about a paper from top robotics/AI venues including RSS, ICRA, IROS, HRI, and Neurips that you find interesting.
Invited speakers include:
- Peter Stone (UT Austin)
- Jean Oh (CMU)
- Andrea Bajcsy (CMU)
- Ahmed Qureshi (Purdue)
- Denise Coke ($NP Designs, AR+Art)
- Heiko Hamaan on 9/28 (University of Konstanz)
- Siddharth Karamcheti (Stanford)
- Aamodh Suresh (Army Research Lab)
- Donald Sofge on 10/19 (Naval Research Laboratory)
- Tariq Iqbal (University of Virginia)
- Necmiye Ozay (University of Michigan)
- Roberto Martin-Martin (UT Austin)
- Amy Zhang (UT Austin)
- Christoforos Mavrogiannis (University of Michigan)
- Sachiko Matsumoto (UC San Diego)
- Hee Rin Lee (Michigan State)
Time and Location:
- Cornell Tech, Bloomberg Center 161, 10:10 – 11:25 AM
- Ithaca Campus, Bill and Melinda Gates Hll G13, 10:10 – 11:25 AM
The learning outcomes for this course include:
- Review seminal robotics papers and use critical thinking to identify, analyze, and evaluate scientific contributions presented.
- Understand state-of-the-art techniques, tools, and algorithms in robotics.
Lectures are on Monday and Wednesday from 10:10 – 11:25 AM ET in Bloomberg 161 and will consist of student lectures and invited talks.
Students are expected to read seminal robotics papers from top robotics/AI venues including RSS, ICRA, IROS, HRI, and Neurips as well as prepare a presentation to present to the class on that topic. Students will give one talk. Group talks are allowed. Students not presenting are expected to read the presented paper, submit questions before lecture, and ask questions to presenters. These sessions are intended to be encouraging and thought provoking. We will take note of students participating in class which will impact students’ participation scores for the class.
Guest speakers are experts working in robotics, AI, ML, and computer vision. Students are expected to ask questions during the Q&A. These talks are remote and will be broadcasted in the lecture room. Lastly, these talks are scheduled in conjunction with the Cornell Robotics Seminar on Thursdays at 2:40pm and students are expected to attend them (see course schedule).
CS 2800 or equivalent, AI/ML course, robotics course, or permission of the instructor.
Final grades are evaluated based on student presentations and class participation as follows:
- Class presentation – 50%
- Class participation – 50%
This course is hybrid by nature given that invited talks are hosted remotely. Students are expected to attend remote guest lectures and in-person student and guest lectures and participate in discussions to be successful in this course. Contact the instructor If you miss a lecture due to an illness or emergency.
Seek help early and often to avoid delays in feedback when issues come up while completing assignments.
If you miss a substantial number of classes due to an on-going illness, please contact Student Disability Services to arrange accommodations and inform the instructor.
Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student’s own work. The policy can be found on the university’s website here: https://theuniversityfaculty.cornell.edu/academic-integrity/.
Students are expected to treat their classmates and course staff with respect. All individuals from different cultural backgrounds, genders, and sexual orientations are welcome here. When students encounter incidents that violate this, they are encouraged to inform the instructors so these issues can be addressed in a timely manner (See Cornell’s Computer Science Community Statement of Values of Inclusion).
Students with Disabilities
Your access in this course is important. Please give me your Student Disability Services (SDS) accommodation letter early in the semester so that we have adequate time to arrange your approved academic accommodations. If you need an immediate accommodation for equal access, please speak with me after class or send an email message to me and/or SDS at firstname.lastname@example.org. If the need arises for additional accommodations during the semester, please contact SDS. You may also feel free to speak with Student & Academic Affairs at Cornell Tech who will connect you with the university SDS office.
Cornell University is committed to supporting students who wish to practice their religious beliefs. Students are advised to discuss religious absences with their instructors well in advance of the religious holiday so that arrangements for making up work can be resolved before the absence.
Supportive Community Statement
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Using Generative AI
Generative AI is becoming a commonly used tool for content generation, summarization, question-answering tasks and more. Students may use Generative AI tools to create their presentations but are required to state what GAI model was used, how it was used, and how they would improve the generated presentation during the discussion portion of student lectures. Students are required to read the paper they present and demonstrate their understanding during the talk.
There are no assignments in this course.