Time: Tuesday 14:00 - 15:30, Thursday 11:00-noon or by
Office: GDC 3.432
|Ricardo Delfin Garcia||ricardo.delfin.garcia@gmail||TTh||1:30-3:30 pm|
|Roy Falik||roy.falik@ut||MWF||2:00-3:00 pm|
|Yuqian Jiang||jiangyuqian@utexas||M||1:00-3:00 pm|
|Rishi Shahemail@example.com||F||11:00 am-2:00 pm|
|Nicholas Walker||nickswalker@icloud||M||2:00-4:00 pm|
|F||11:00 am-2:00 pm|
Class Diary (including links to slides and readings)
- 12/13 Final Project Presentations! [SCHEDULE]
- 10/3 and after: work in the lab on your projects
- 9/29 Project Proposal Presentatios II
Slides (announements): [PDF]
- 9/27 Project Proposal Presentatios I
- 9/22 Machine Learning and your projects
- 9/20 No Class -- work on project proposals
- 9/15 Robot Demos and QA
- 9/13 Student Preliminary Project Proposal Presentations
- 9/6 Reinforcement Learning + Project Ideas
Pac-Man RL Code from class: [ZIP]
- 9/1 Guest Research Presentations
Guest Talk 1: Fast and Correct Systems by Dr. Vijay Chidambaram
Guest Talk 2: Building an Intelligent 3D Map by Chia-Chen (Jennifer) Hsu
Guest Talk 3: Finding Relevant Images by Dr. Garrett Warnell
- 8/30 Robot Tutorials
- 8/25 Class Introduction
Preliminary Project Proposal ''Presentations'': September 13th Project Proposal Presentations: September 27th and 29th [GUIDELINES] Project Proposal Writeup due: September 30th Progress Report 1 due: November 2nd [GUIDELINES]
Final Project Presentations: December 13th 2:00 - 5:00 pm
Final Project Report: December 14th [GUIDELINES]
The focus of this course is on research involving intelligent and autonomous robots. In particular, specific topics covered this semester will include human-robot interaction, computational perception, and developmental robotics. Throughout the semester, the students will use the mobile robots that are currently part of the building wide intelligence (BWI) project. The idea is to have a pervasive intelligence throughout the building, in the form of robots that will perform a variety of tasks, such as leading people to their destinations or locating a person in the building.
The main goal of this course is to complete a small research project, advancing the abilities of the current BWI system.
Participation in the class discussions will also form a significant part of the grade. Class meetings will consist of discussions based on assigned readings and updates on project progress.
Philosophy and Goal
The foremost goal of this course is to expose the student to the full range of activities required of a real-life computer science researcher. It turns out that computer scientists rarely read textbooks, sit silently in lectures, work on programming assignments with correct and complete answers, or take exams. Rather, they
- read about and critically assess original research;
- speak in public;
- collaborate effectively with peers;
- devise solutions and/or approaches to open-ended problems; and
- write about these solutions and/or approaches.
This course presents an opportunity for students to help decide whether they would enjoy going on to graduate school and an eventual career as a computer science researcher. In particular, students will be required to read published research papers, write brief reactions to them, participate in class discussions, propose and execute a solution to a challenging open-ended problem, and write about their work. They will be given an opportunity to collaborate with other students on the final project.
Grades will be based on
- class participation (10%);
- written responses / misc. assignments (10%)
- a final project. (80%)
Students should post responses to the readings on Canvas. Credit will be based on evidence that you have done the readings carefully. The response should include a summary of the reading along with any of the following:
- Insightful questions;
- Clarification questions about ambiguities;
- Comments about the relation of the reading to previous readings;
- Critiques on the research;
- Critiques on the writing style or clarity;
- Thoughts on what you would like to learn about in more detail;
- Possible extensions or related studies;
- Thoughts on the paper's importance; and
- Summaries of the most important things you learned.
A strong interest in the question, ``What is intelligence and how can it be implemented in a physical robot?''
For best results take two lectures weekly. Common side effects may include sleepless nights, broken robots, nervousness, and banging head on keyboard. Frequent visits to the mentors and the TA have been shown to alleviate some of those symptoms. Talk to your instructor if this class is right for you.
Text and Website
There is no textbook for this course. Instead, relevant research papers will be initially assigned, and later chosen by the students following their interests.
Academic Dishonesty Policy
All work ideas, quotes, and code fragments that originate from elsewhere must be cited according to standard academic practice. Students caught cheating will automatically fail the course. If in doubt, look at the departmental guidelines and/or ask.
Notice about students with disabilities
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. To determine if you qualify, please contact the Dean of Students at 471-6529; 471-4641 TTY. If they certify your needs, I will work with you to make appropriate arrangements.
Notice about missed work due to religious holy days
A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.
This course was previously taught by Matteo Leonetti who developed some of the course content, assignments, etc. If you enjoyed the course, feel free to send him a thank you note. Some of the lectures on Computational Perception and Developmental Robotics were originally developed and/or influenced by Professor Alexander Stoytchev at Iowa State University. If you enjoy them, feel free to send a thank you as well.
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