CS 6756, Cornell University, Fall 2022

Learning for Robot Decision Making

Instructor: Sanjiban Choudhury

Lectures: Tue / Thurs, 9.40 - 10.55 a.m., Thurston Hall 203


Advances in machine learning have fueled progress towards deploying real-world robots from assembly lines to self-driving. Learning to make better decisions for robots presents a unique set of challenges. Robots must be safe, learn online from interactions with the environment, and predict the intent of their human partners. This graduate-level course dives deep into the various paradigms for robot learning and decision making. We look at:

  1. Interactive no-regret learning as a fundamental framework for handling distribution shifts, hedging, exploration/exploitation.
  2. Imitation learning from various modes of interaction (demonstrations, interventions) as a unified, game-theoretic framework.
  3. Practical reinforcement learning that leverages both model predictive control and model-free methods.
  4. Open challenges such as safety, causal confounds and offline learning.
This course focuses on algorithms, lessons from real world robotics and features a strong programming component.


Schedule (Tentative)

Date Lecture Preread Resources
08/23/22 Introduction to Robot Learning [slides, notes] Trailer
08/25/22 Interactive Online Learning [slides, notes] Shai Shalev-Shwartz (Pg.108-111) Arora et al. "Multiplicative Weights", Generalized Weighted Majority video
08/30/22 Markov Decision Process I [slides, notes, python notebook]
(Assignment 1 Released)
MACRL (Pg.9-12) Dan Klein's slides I
09/01/22 Markov Decision Process II [slides, notes, python notebook] MACRL (Ch 5) Dan Klein's slides II
Model Predictive Control
09/06/22 Linear Quadratic Regulator: The Analytic MDP [slides, notes, python notebook] MACRL (Ch 2, Pg. 23-27) Underactuated robotics, Ch. 8, History of Optimal Control
09/08/22 Iterative Linear Quadratic Regulator [slides, notes] MACRL (Ch 2, Pg. 28-33) iLQR paper , DDP for helicopter flight
09/13/22 Constraints and Games [slides, notes] MACRL (Ch 4) Gordon's notes on Lagrange, ALTRO: AuLa + iLQR,
09/15/22 Practical Model Predictive Control [slides, notes] Full scale helicopter flight paper
Imitation Learning
09/20/22 Imitation Learning: Feedback and Covariate Shift [slides, notes]
(Assignment 2 Released)
MACRL (Ch 6, Pg. 53-57) Three regimes of covariate shift
09/22/22 DAgger: A Reduction to No-Regret Learning [slides, notes] MACRL (Ch 6, full) DAGGER , Agnostic SysId
09/27/22 Imitation Learning as Inferring Latent Expert Values [slides, notes] PDL Proof EIL, HG-DAGGER , Youtube lec
09/29/22 Inverse Reinforcement Learning: From Maximum Margin to Maximum Entropy [slides, notes] MACRL (Ch 6, full) LEARCH , MaxEntIRL , Youtube lec
10/04/22 Distribution Matching, Maximum Entropy, GANs, and all that [slides, notes] MACRL (Ch 7, full) Guided cost learning , f-divergence IL , Youtube lec
10/06/22 Imitation Learning: The Big Picture [ slides , notes] Of Moments and Matching , Youtube lec
Reinforcement Learning
10/13/22 Reinforcement Learning: From Games to Robotics [ slides , notes]
10/18/22 Temporal Difference Learning (Assignment 3 Released) [ slides , notes] MACRL (Ch 9, full) Sutton&Barto (Ch. 5, 6) , DQN , Rainbow DQN
10/20/22 Approximate Dynamic Programming [ slides , notes] MACRL (Ch 8, full)
10/25/22 Black-box vs White-box Policy Optimization [ slides , notes] MACRL (Ch 10, full)
10/27/22 Halloween Special: Nightmares of Policy Optimization [ slides , notes] MACRL (Ch 11, full)
11/01/22 Actor Critic Methods [ slides , notes] MACRL (Ch 11, full)
11/03/22 Planning with Inaccurate Models [ slides , notes]
11/08/22 Dealing with Uncertainty I [ slides , notes] (Extended Abstracts Due)
11/10/22 Dealing with Uncertainty II [ slides , notes]
11/15/22 Learning for Robot Decision Making Recap [ slides , notes]
Open Challenges
11/17/22 Causal Confounds in Sequential Decision Making (Guest Lecture by Gokul Swamy) [ slides ]
11/22/22 Interactive Forecasting (Sanjiban) [ slides , notes]
11/29/22 Offline Reinforcement Learning (Dhruv)
12/01/22 No class
12/06/22 No class
12/08/22 No class
12/13/22 Project presentations
12/15/22 Project presentations

Assignments and Final Project

There will be a total of 3 assignments, each involving a programming component and some theory. All assignments must be done individually. As the course progresses, we will release each assignment in the links below with starter code on Github.

There will also be a final project. This is your chance to get creative and apply what you have learned! For the project, you may work in groups of up to two people. There will be three deliverables - an extended abstract, a final report and a final presentation. The abstract and report will should NeurIPS format. You are welcome to select any topic that may be relevant to your research, an open problem of interest or from a list of potential projects that we will share. We will also have a best paper award as judged by your peers!



  • Course Website: The ONE true hub for all information. Please check this frequently and surface any errors or sources of confusion.
  • Ed: The discussion forum where all announcements are sent, where all student-TA and student-student communications occur.
  • Gradescope: Where all assignments and projects are submitted.
  • Canvas: Limited to no use.


Relevant Textbooks

The course is extensively based off of the following book:

This a live book that is constantly being updated. Periodically check this link for newer versions. Students are encouraged to send feedback and corrections to the instructor.

Other helpful books and notes:

Courses / Lectures



Sanjiban Choudhury



Office Hours:
Tue 11-12 pm, Thurs 12.30 - 1.30 pm Gates 413B

Dhruv Sreenivas

Teaching Assistant


Office Hours:
Mon / Wed 11-12 pm, Rhodes 400

Assignments, lectures, and ideas on this syllabus are partially adapted from Drew Bagnell course at Carnegie Mellon University. We thank Drew for insightful discussions and suggestions for how to structure the course.


Learning Outcomes

  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.


For graduates: This course is open to both CS and non CS PhD and MS. For non CS PhD and MS students, please add yourself to the waitlist or send an email to cs-course-enroll@cornell.edu. For undergraduates: A prerequisite is Machine Learning (CS 4780). Students should have some background in linear algebra and probability. Familiarity with Python and neural network libraries (Pytorch, TensorFlow) is required.

Grading Policy

Here’s a breakdown of grades:

Component Details %Grade
Assignments 3 assignments, each 15% 45%
Final Project Extended Abstract: 5%,
Final Report: 20%,
Final Presentation: 15%
Participation In-class participation and Ed discussions 10%
Total 100%

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.

Late Policy

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. Any assignment turned in late will incur a reduction in score by 33% for each late 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 online via a private post on Ed within 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.

Academic Integrity

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

Diversity, Equity and Inclusion

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.

COVID-19 related issues

For students becoming ill or needing to quarantine during the semester, we will address your needs on a case-by-case basis. Please contact Dr. Choudhury if you have any concerns.


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