Introduction to Perception and Robotics

Syllabus

Learning Objectives

Upon completion of this course, students will be able to:

• Describe and explain what robots are and what they can do
• Describe mathematically the position and orientation of objects and how they move
• Develop a control architecture for a mobile robotic system
• Implement navigation and localization algorithms based on sensor fusion and environment representation
• Write moderately involved programs in Python and Java to control a robotic system
• Construct, program, and test the operation of a robotic system to perform a specified task

Prerequisites

The only formal prerequisite is CS1332 Data Structures & Algorithms. Prior knowledge of fundamentals of linear algebra and probability is helpful, but not required. Background in AI and Machine Learning is not assumed. The course requires access to a laptop. If you don’t have access to a laptop, please contact the instructor ASAP. All programming assignments will be completed in Python.

Important Links:

• Piazza! This should be your first stop for questions and announcements.
• Gradescope is where you will submit projects. It is also available from the Canvas menu.
• Canvas will be used for occasional announcements and disseminating the lecture videos.

Lecture Delivery and Grading Components

Important: You cannot take this class unless you can make the regularly scheduled class periods at 3.30pm.

• Lectures will be live on Microsoft Teams! You have already been added to the Team for this class. Please email us if you are unable to access it from your GT Teams account.
• Questionnaires: To reinforce the lecture and prepare for class time, the students will be required to answer a short questionnaire on Canvas about the two lectures from the preceding week. We shall release the questionnaires every Thursday and you’ll have until Sunday night to complete them. This will count for 10% of your grade. We anticipate about 11-12 questionnaires throughout the semester.
• Quizzes: there will be 6 modules, as indicated on the schedule. At the end of each module, there will be an online quiz worth 6% of your grade each, for a total of 30% (we will pick the best 5 out of 6). Quizzes will have a combination of multiple choice and short essay questions, as appropriate.
• Projects: Project deliverables are the most significant component of this course and will make up 60% of your final grade. There will be 6 projects based on the 6 modules, and you will submit your code and reports on gradescope.

Grading

The grading distribution is:

Project Late Policy

The late policy for projects is to linearly decrease the maximum score from 100 to 0, starting at the submission deadline (Friday, 23:59:59) and ending the following Monday, 23:59:59. Thus, the maximum possible score decreases continuously from 100 to zero over the 72 hour period following the submission deadline (e.g., if you submit your project exactly 29 hours and 30 minutes late, you would have a maximum possible score of 59.02777777777778%).

Academic Integrity

Academic dishonesty will not be tolerated. This includes cheating, lying about course matters, plagiarism, or helping others commit a violation of the Honor Code. Plagiarism includes reproducing the words of others without both the use of quotation marks and citation. Students are reminded of the obligations and expectations associated with the Georgia Tech Academic Honor Code and Student Code of Conduct, available online at www.honor.gatech.edu.

You are expected to implement the core components of each project on your own, but the extra credit opportunities often build on third party data sets or code. That’s fine. Feel free to include results built on other software, as long as you are clear in your hand-in that it is not your own work.

You should not view or edit anyone else’s code. You should not post code to Piazza, except for starter code / helper code that isn’t related to the core project.

Learning Accommodations

If needed, we will make classroom accommodations for students with documented disabilities. These accommodations must be arranged in advance and in accordance with the ADAPTS office (www.adapts.gatech.edu).

Contact Info and Office Hours:

Use Piazza to ask questions and seek clarifications. If you have a very specific question (related to your grade etc.) or a question that involves your personal information, you can make a private post on Piazza.

The TA office hours will be announced very soon, along with the location, in a pinned post on Piazza. Hope you know where the 3630 Lab is (that’s right, we have a lab just for 3630!) - in the basement of the College of Computing building. We have some really cool fairy lights and a large television screen. Do come for our TA hours and get all your doubts clarified!

• Rachit Bhargava
• Bikram Boote
• Ishan Chadha
• Jerred Chen
• Youssef Elmougy
• Abhinav Gupta
• Asha Gutlapalli
• Abhineet Jain
• Zhen Jiang
• Lixing Liu
• Vivek Mallampati
• Junyan Mao
• Andrew Messing
• Adam Patni
• Maya Rajan
• John Yi

Acknowledgements

The materials from this class rely significantly on slides prepared by other instructors. Each slide set and assignment contains acknowledgements. Feel free to use these slides for academic or research purposes, but please maintain all acknowledgements.