Learning for Robot Decision Making

1. Formulate various robot decision making problems, e.g. robot manipulation, self-driving, assistive robots, as a Markov Decision Problem (MDP).
2. Solve different types of MDPs by applying appropriate techniques, e.g. model predictive control (iLQR), value / policy iteration, black-box policy search.
3. When a MDP is unknown, apply appropriate learning techniques, e.g. imitation learning, model-free / model-based reinforcement learning.
4. Analyze sample-complexity and performance bounds for various robot learning algorithms using techniques from no-regret online learning.
5. Develop, evaluate and deploy robot learning algorithms in various robotics applications.

Here’s a breakdown of grades:

Assignments must be done individually. Each assignment will require students to turn in a writeup and code in Gradescope. It is acceptable for students to discuss problems with each other; it is not acceptable for students to share answers or code. Please indicate on each homework with whom you collaborated with and what online resources you used.

The final project can be done in groups of up to 2. There are three deliverables - an extended abstract, a final report, and a final presentation. The abstract and report will should NeurIPS format. We will share the rubric for how these will be evaluated in due time, but they will roughly be along the lines of NeurIPS reviewer guidelines. For groups of more than one, we will expect a short paragraph to explain the role of each group member along with the final report. We will also have a best paper award as judged by your peers!

Research has demonstrated that the best learning occurs when the learner is actively involved. We will have frequent opportunities for students to work together during lectures. We expect you to come to class prepared to focus, interact with classmates, and participate in the activities. We also expect you to participate in discussions on Ed and create an engaging environment.

Assignments must be submitted by the posted due date. You are allowed up to 3 total LATE DAYs for any deliverable throughout the entire semester. If you exceed the late days, assignments will incur a reduction in score by 33% for each extra day. The final presentation must be presented on time, no late policy applies. Regrade requests, if the case is strong and a significant number of points are at stake, should be submitted via Gradescope one week of when a deliverable is returned to the student. You must provide a justification for the regrade request.

In case of a legitimate situation or medical emergency that arises during the semester that is going to hinder your ability to complete the work on time, contact Prof. Choudhury as soon as possible. Extensions (beyond the already assigned slip days) will be granted only in exceptional circumstances, such as documented illness, not for situations such as job interviews or large workloads in other courses.

This course adheres to all aspects of Cornell's Code of Academic Integrity. Any work presented as your own must be your own, with no exceptions tolerated. All violations of this policy will result in a penalty depending on the severity. The penalty may be a failing grade on the relevant assignment or exam, or a failing grade in the class. The code can be found at: http://cuinfo.cornell.edu/aic.cfm

The work you do for CS 6756 consists of writing code and natural language descriptions. To some extent, the new crop of “generative AI” (GAI) tools can do both of these things for you. However, we require that the vast majority of the intellectual work must be originated by you, not by GAI. You may use GAI to look up helper functions, or to proofread your text, but clearly document how you used it.

In this class, for every assignment and final project, you can choose between two options:

Option 1: Avoid all GAI tools. Disable GitHub Copilot in your editor, do not ask chatbots any questions related to the assignment, etc. If you choose this option, you have nothing more to do.

Option 2: Use GAI tools with caution and include a one-paragraph description of everything you used them for along with your writeup. This paragraph must:

1. Link to exactly which tools you used and describe how you used each of them, for which parts of the work.
2. Give at least one concrete example (e.g., generated code or Q&A output) that you think is particularly illustrative of the “help” you got from the tool.
3. Describe any times when the tool was unhelpful, especially if it was wrong in a particularly hilarious way.
4. Conclude with your current opinion about the strengths and weaknesses of the tools you used for real-world compiler implementation.

Below we provide some guidelines for what is / is not ok when using GAI for this class:

1. Example of something that is allowed: You write the initial code / writeup. You then use GAI to debug the code / improve writing flow. You do not use the system's output to add extra content.
2. Example of something that is definitely not allowed: You essentially use GAI to generate most of the code / writeup, even if you later post-edit and correct the output.
3. Example of something that is OK but requires special treatment: You start with procedure in 1. But, the GAI suggests good points that you hadn’t thought of before, or makes you realize that a point you had made isn’t quite right. You may include this new material, but follow the guidelines above to document the use.

Students in this course come from a variety of backgrounds, abilities, and identities. In order to ensure an environment conducive to learning, all members of the course must treat one another and the course staff with respect. If you feel your needs are not being adequately accommodated by the other students or instruction staff, please contact Prof. Choudhury.

Zoom recordings of lecturers are not available for absences, including absences due to illness.

If you are a close contact with someone who is diagnosed with COVID19, even if you do not experience symptoms, you should test yourself [and you may want to request masking for five days after exposure regardless of the outcome].

1. Do not come to class.
2. Email Dr.Choudhury before class starts to let [me, TA] know that you are not coming.
3. Get an antigen test.
4. If the test is negative, you may return to the next class. Please wear a mask until your symptoms are gone, even if you test negative.
5. If your antigen test is positive, you must immediately upload the result to Daily Check: dailycheck.cornell.edu. This action will trigger instructions and a letter of temporary accommodation. You must forward the temporary accommodations email to Dr. Choudhury to receive an accommodation. (The system will not send it for you.) Once Dr. Choudhury receive the letter, we will provide guidance on how you should keep up with material for the next 5 days.
6. You may return to class on day 6 provided you are asymptomatic. You must wear a mask through the end of day 10 from your first onset of symptoms.

If you have a disability-related need for reasonable academic adjustments in this course, please reach out to Student Disability Services to guide us through next steps. If you are experiencing undue personal or academic stress during the semester, we encourage you to reach out to the instructor for support.

We encourage you to check out the comprehensive set of resources compiled by EARS, Reflect, Cornell Minds Matter, and Body Positive Cornell: Cornell Mental Health Resources Guide 2022-23