Course Information

Lectures: TuTh 10-11:30am in KINSC H108
Labs: Wednesdays 11:00am-12:00pm or 1:00-2:00pm in H110
Professor: Thao Nguyen (she/her)
Office hours: Tuesdays 2-4pm in L303 (or schedule an appointment with me)
TAs: Owen Bluman, Zenas Boamah, Matt Feinstein
TA hours: Mondays 9-11pm in H110, Tuesdays 7:30-9:30pm in H204, Thursdays 8-10pm in H110
Piazza: CS369

The prerequisites for this course are Data Science and Linear Algebra.

This course presents an overview of robotics in practice and research with topics including kinematics, control, motion planning, perception, reinforcement learning, and human-robot interaction. Students will implement algorithms to enable a robot to learn about and interact with the physical world.

Textbook:

You do not need to purchase a textbook for this course. We will draw from several online textbooks, as well as supplemental online readings and research papers.

See the Schedule for each week's reading assignment. The schedule is tentative and subject to change throughout the semester.

Schedule (Tentative)

WEEK	DAY	TOPIC & READING	LECTURES	LAB & MEMOS
1	Jan 20	Introduction to Robotics What is a robot? Types of robots Robotic applications Robot configuration	Slides 1	Lab 1: Robot Assembly and Setup (due Jan 28 at 11:59pm)
	Jan 22		Slides 2
2	Jan 27	Kinematics Coordinate transformations Forward kinematics Inverse kinematics (IK) Jacobians Iterative IK Reading:	Slides 3	Lab 2: Forward and Inverse Kinematics (due Feb 03 at 11:59pm)
	Jan 29		Slides 4
3	Feb 03	Control Theory Controllers PID control Reading:	Slides 5	Lab 3: Iterative IK and Control (due Feb 10 at 11:59pm) Last day to drop (Feb 06)
	Feb 05		Slides 6
4	Feb 10	Motion Planning Configuration space Environment representations Graph search algorithms Sampling-based algorithms Reading:	Slides 7	Lab 4: Motion Planning (due Feb 24 at 11:59pm)
	Feb 12		Slides 8
5	Feb 17	Perception Pinhole camera model Object detection Convolutional neural networks (CNNs) Reading: From Stanford Computer Vision course: Camera models section 1-4 Backpropagation CNNs	Slides 9
	Feb 19		Slides 10
6	Feb 24	Localization I Belief estimation Probabilistic algorithms Reading:	Slides 11	Lab 5: Perception (due Mar 04 at 11:59pm)
	Feb 26		Slides 12
7	Mar 03	Localization II Motion models Sensor models Reading:	Slides 13
	Mar 05
	Mar 10	Spring Break
	Mar 12
8	Mar 17	Robot Learning Imitation learning Behavioral cloning Reading: An Invitation to Imitation by J. Andrew Bagnell	Slides 14	Lab 6: Localization (due Apr 01 at 11:59pm)
	Mar 19		Slides 15
9	Mar 24	Reinforcement Learning (RL) Markov decision process (MDP) Rewards and policies Types of RL algorithms Policy gradient Reading: OpenAI's Spinning Up in Deep RL intro part 1-3	Slides 16	Final Project Proposal (due Apr 03 at 11:59pm over email)
	Mar 26		Slides 17
10	Mar 31	Human-Robot Interaction Natural language processing Language grounding Reading:	Slides 18
	Apr 02		Slides 19
11	Apr 07	Robot Ethics Safety Transparency Privacy and security Labor displacement Accountability Reading:
	Apr 09
12	Apr 14	Research Paper Presentations
	Apr 16
13	Apr 21	Research Paper Presentations
	Apr 23
14	Apr 28	Project Presentations Final project presentations Robot demonstrations		Last day to pass/fail (May 01)
	Apr 30

Grading Policies

Grades will be weighted as follows:

45%	Lab assignments
25%	Final project (including presentation and robot demonstration)
15%	Research paper presentation
15%	Attendance & participation

Both lecture and lab attendance are required, and missing class will quickly affect your participation grade.

Quizzes and Exams

In lieu of quizzes this semester, we will have short excercises during class (to work on and discuss, not turn in). Be ready to work on these exercises by completing the weekly reading before class on Tuesdays.

You will also read and present research papers in class in place of a midterm. In lieu of a final exam, there will be a final project and associated presentation.

Labs

Lab assignments will generally be released Tuesday night, introduced on Wednesday during lab, and due the following Tuesday at midnight. There will sometimes be group-programming exercises as part of the lab, and lab in general is a time to build community around the course and the material. Please note that I will often be off campus on Monday, and make use of office hours (both mine and the TAs) and Piazza for questions.

Weekly Lab Sessions
Lab A 11:00am—12:00pm Wednesdays	Nguyen	H110
Lab B 1:00pm—2:00pm Wednesdays	Nguyen	H110

Handing in labs: Lab assignments are submitted electronically and managed using GitHub Classroom. You may submit your assignment multiple times, but each submission overwrites the previous one and only the final submission will be graded. Some of the programming/lab assignments may be in groups. There may also be some written assignments that will have specific instructions for handing in.

Late Policy: Late work will not be accepted. Even if you do not fully complete a lab assignment, you should submit what you have done to receive partial credit. In the case of an emergency or ongoing personal issue, please contact your Class Dean. If your Class Dean notifies me of the issues, then we can arrange an accommodation.

Academic Integrity

From the faculty:

In a community that thrives on relationships between students and faculty that are based on trust and respect, it is crucial that students understand a professor's expectations and what it means to do academic work with integrity. Plagiarism and cheating, even if unintentional, undermine the values of the Honor Code and the ability of all students to benefit from the academic freedom and relationships of trust the Code facilitates. Plagiarism is using someone else's work or ideas and presenting them as your own without attribution. Plagiarism can also occur in more subtle forms, such as inadequate paraphrasing, failure to cite another person's idea even if not directly quoted, failure to attribute the synthesis of various sources in a review article to that author, or accidental incorporation of another's words into your own paper as a result of careless note-taking. Cheating is another form of academic dishonesty, and it includes not only copying, but also inappropriate collaboration, exceeding the time allowed, and discussion of the form, content, or degree of difficulty of an exam. Please be conscientious about your work, and check with me if anything is unclear.

Please also note the CS Department Collaboration Policy.

More details for this course:

Under no circumstances may you hand in work done with (or by) someone else under your own name. Your code should never be shared with anyone; you may not examine or use code belonging to someone else, nor may you let anyone else look at or make a copy of your code. This includes, but is not limited to, obtaining solutions from students who previously took the course or code that can be found online. You may not share solutions after the due date of the assignment.

Discussing ideas and approaches to problems with others on a general level is fine (in fact, we encourage you to discuss general strategies with each other), but you should never read anyone else's code or let anyone else read your code. All code you submit must be your own with the following permissible exceptions: code distributed in class, code found in the course text book, and code worked on with assigned partner(s). In these cases, you should always include detailed comments that indicates on which parts of the assignment you received help, and what your sources were.

GitHub copilot (or any other software for automatically generating code) is not allowed for this course, until the final project. The reasoning behind this decision is that code generation tools often create code that is not well understood by the user. Often this code becomes incorrect in the larger context of the program. However, for the final project you are welcome to use GitHub copilot, and you'll be asked to reflect on your experience.

Piazza

We will be using Piazza, an online Q&A forum for class discussion, to help with labs, clarifications, and announcements. You will receive an email invitation to join CMSC H369 on Piazza. If you don't, please let me know.

Piazza is meant for questions outside of regular meeting times such as office hours, class, and lab. Please do not hesitate to ask and answer questions on Piazza, but keep in mind the following guidelines:

1. Piazza should be used for ALL content and logistics questions outside of class, lab, and office hours. Please do not email me your code or extended questions about the assignments.
2. If there is a personal issue that relates only to you, please email me.
3. We encourage non-anonymous posts, but you may post anonymously (to your classmates, not the instructors).
4. Do not post long blocks of code on Piazza - if you can distill the problem to 1-2 lines of code and an error message, that's fine, but try to avoid giving out key components of your work.
5. By the same token, when answering a question, try to give some guiding help but do not post code fixes or explicit solutions to the problem.
6. Posting on Piazza counts toward your participation grade, both asking and answering!

Haverford Academic Accommodations Statement

For details about the accommodations process, visit the Access and Disability Services website.

We are committed to partnering with you on your academic and intellectual journey and recognize that you bring many strengths, perspectives and strategies as you navigate this journey. We also recognize that your ability to thrive academically can be impacted by your personal well-being and that stressors may impact you over the course of the semester. If the stressors are academic, we welcome the opportunity to discuss and address those stressors with you in order to find solutions together. If you are experiencing challenges or questions related to emotional health, finances, physical health, relationships, learning strategies or differences, or other related topics, we hope you will consider reaching out to the many resources available on campus. These resources include CAPS (free and unlimited counseling is available), Office of Academic Resources, Writing Center, Student Diversity Equity and Access Team, Health Services, Professional Health Advocate, Religious and Spiritual Life, the Office of Multicultural Affairs, the GRASE Center, and the Dean's Office. Additional information can be found here.

Additionally, Haverford College is committed to creating a learning environment that meets the needs of students from a wide range of backgrounds and experiences, and providing equitable access to students with disabilities. If you have (or think you may have) a disability related to mental health, chronic health, neurological state, and/or physical condition – please contact the Office of Access and Disability Services (ADS) at hc-ads@haverford.edu. It is never too late to request ADA accommodations – our bodies and circumstances are continuously changing. Please know that all inquiries and health-related information is handled in a sensitive and confidential manner.

Students who have already been approved to receive academic ADA accommodations and want to use these in this course should share their accommodation letter and make arrangements to meet with me as soon as possible to discuss how their accommodations will be implemented in this course. Please note that accommodations are not retroactive and require advance notice in order to successfully implement.

If, at any point in the semester, a disability or personal circumstances affect your learning in this course or if there are ways in which the overall structure of the course and general classroom interactions could be adapted to facilitate full participation, please do not hesitate to reach out to us.

It is a state law in Pennsylvania that individuals must be given advance notice that they may be recorded. Therefore, any student who has a disability-related need to audio record this class must first be approved for this ADA accommodation by Access and Disability Services and then must communicate approval to me. I will then make a general announcement to the class that audio recording may occur while respecting students’ right to privacy by not identifying the individual(s).

Haverford Title IX Statement

Haverford College is committed to fostering a safe and inclusive living and learning environment where all can feel secure and free from harassment. All forms of sexual misconduct, including sexual assault, sexual harassment, stalking, domestic violence, and dating violence are violations of Haverford's policies, whether they occur on or off campus. Haverford faculty are committed to helping to create a safe learning environment for all students and for the College community as a whole. If you have experienced any form of gender or sex-based discrimination, harassment, or violence, know that help and support are available. Staff members are trained to support students in navigating campus life, accessing health and counseling services, providing academic and housing accommodations, and more.

The College strongly encourages all students to report any incidents of sexual misconduct. Please be aware that all Haverford employees (other than those designated as confidential resources such as counselors, clergy, and healthcare providers) are required to report information about such discrimination and harassment to the Bi-College Title IX Coordinator.

Information about the College's Sexual Misconduct policy, reporting options, and a list of campus and local resources can be found on the College's website here.

Useful Links

Official Python style guide
Python 3 Documentation