Course Syllabus

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Welcome!

What does this course offer you?

Algorithmic Foundations of Robotics explores the algorithms that help robots operate in the real world from autonomous cars to manipulators in factories. It covers a range of topics including forward/inverse kinematics, motion planning, simultaneous localization and mapping (SLAM), and optimal control. Students will learn about the algorithms and mathematical principles that enable robots to perceive, reason, and act in the world. By the end of the course, students will have an understanding of the fundamental concepts that drive robotic systems, and will be equipped with the skills to design and implement their own robotic algorithms.

Learning Objectives

  1. Identify fundamental abstractions used by mobile robots in the wild (localization, planning, control)
  2. Understand how the fundamental abstractions interact
  3. Analyze algorithmic trade-offs within each abstraction.
  4. Apply the above abstractions to enable a simulated mobile robot to navigate safely and efficiently, using Python and the Robot Operating System (ROS). Conduct principled robot experiments to diagnose and fix bugs across the mobile robot software stack. 

Prerequisites

No robotics experience is assumed. Previous experience in robotics, filtering or controls will be helpful. The following skills are necessary for this class

  • Data structure and algorithm analysis 
  • Programming. Python will be used primarily in this course, so experience with it is preferred.
  • Math: Linear algebra, vector calculus, and probability.

How are you, exactly, going to achieve this?

We will have readings associated with most lectures. You should read this material and come prepared to class. We will have selected readings from Probabilistic Robotics, see Course Resources.

Other readings will come from research papers or notes provided by the instructor.

Programming projects will provide you with an opportunity to implement algorithms studied in the course. You will implement these assignments independently to help improve your own skills in programming robotics algorithms. You make work with other students at the level of "whiteboard discussion" to help understand the assignments, but all work turned in must be your own. Please list any individuals you worked with on assignments and correctly cite any other resources used. You may not use AI tools such as, ChatGPT, Copilot, and other wiz bang models, to make or help you in solving the homework problems.

How will you (and the teaching staff) know if you are making progress in your learning?

The best way to understand your progress is to actively engage in class and work to answer questions yourself. Each class, you will be provided time to engage with the professor and your classmates to solve problems. If something isn't clear, then ask about it! If you are confused, then many others in the class likely are as well.

We will provide feedback on programming assignments quickly to aid in understanding. Office hours by the professor and TA also serve as a great time to go and get individual feedback on your progress if you are uncertain.

Course Time and Location

We will meet Tuesday and Thursday (unless stated otherwise on the calendar) from 9:10AM to 10:30AM Mountain Time. 

Location: M LI 1725

Teaching Staff, Helps Hours, & Contact

Students can meet with the teaching staff during the office hours listed below or by appointment.

Instructors: 

Griffin Tabor; Help Hours: Tuesday 10:30AM - 11:30AM Library study room 1750A-D, Wednesday 3:00-4:00pm MEB 3147 and by appointment

Tucker Hermans; by appointment MEB 2164

Before sending a message to the teaching staff, please first look on the website for the information. If it is not found there or not clear, then think about asking a question on piazza. This will enable other students to get the same information and decrease the workload of both students and the teaching staff.

Also, please ask relevant questions in class!

If you think your question is better handled privately with the teaching staff, then please contact us using the piazza messaging system. Direct emails not through piazza will generally not be read or responded too. Additionally, you should by default include the instructor and TAs on the message to keep everyone on the same page and increase the timeliness of responses.

Grading

Grades will be assigned based on the scores received on programming projects comprising 40%, the final project 10%, the midterm and final exam comprise 45%, and the final 5% assigned to in class activities. 

Final grades will be determined based on overall performance of students in the class. A minimum of 93% will guarantee an A, but this threshold may be moved to a lower value depending on overall performance on assignments.

Late Policy

Please let the teaching staff know as early as possible if you need an extension on any assignments to accommodate issues.

If you don't have extenuating circumstances you have a total of 3 extra days you can elect to use as you see fit to turn in assignments late (for example you could turn in assignments 1, 2, and 3 late by 1 day each or turn in assignment 3,  3 days late, etc). You need to notify in a comment on your submission if you are using one of your late days.

Any other late submissions will be discounted by 10% for being a day late up to a total of 30% off (i.e. at 3 days late). You have 3 days after the deadline to submit before the submissions close for good and you will receive a 0.

Regrade Requests

Any regrade requests must be submitted via gradescope within 1 week of grades being released. We will not address regrade requests made at the end of the semester for assignments that did not have the grade released within the last week.

Further Course Administrative Information

Course Summary:

Date Details Due