Instructor	Justin W. Hart
Class Times	Tuesday & Thursday	3:30-5:00pm
Classroom	Welch (WEL) 2.128
Laboratory	Gates-Dell Complex (GDC) 3.414
Course Syllabus	[PDF]

	Name	Email	Office Hours
Instructor	Justin W. Hart	hart@cs.utexas.edu	Monday	4:00pm-5:00pm
GDC 3.402 or BWI lab			Thursday	5:15pm-6:15pm
Teaching Assistant	Yuqian Jiang	jiangyuqian@utexas.edu	Tuesday	10:30am-11:30am
GDC 3.504A or BWI lab			Thursday	1:30pm-2:30pm
Mentors
	Abrar Anwar	abraranwar123@gmail.com	Tuesday/Thursday	5:00pm-6:00pm
	Ethan Brown	ethane98@yahoo.com	Wednesday	2:00pm-4:00pm
	David Chen	xiangweichen99@gmail.com	Wednesday	10:30am-12:30pm
	Jackie Deans	jacqueline.deans@utexas.edu	Tuesday/Thursday	2:00pm-3:00pm
	Blake Holman	blake.holman@utexas.edu	Thursday	4:30pm-6:30pm
	Jeffrey Huang	jeffreyhuang23@gmail.com	Monday	4:30pm-6:30pm
	Bonny Mahajan	bm.bonnymahajan@gmail.com	Monday	2:00pm-4:00pm
	Lucinda Nguyen	lucinda.onguyen@gmail.com	Wednesday	11:00am-1:00pm
	Sydney Owen	seowen@nctv.com	Thursday	11:00am-1:00pm
	Mayuri Raja	mraja7@utexas.edu	Monday/Wednesday	11:00am-12:00pm
	Connor Sheehan	c-she@utexas.edu	Monday	3:00pm-5:00pm
	Stone Tejeda	stonetejeda@utexas.edu	Tuesday/Thursday	5:00pm-6:00pm

This class provides students with an understanding of modern research in the areas of robotics, artificial intelligence, and human-robot interaction. It is the first part of a two-course sequence, and serves in-part as preparation for the more complex work in CS 378: FRI II. In FRI II, students pursue a semester-long robotics research project. In this course, students learn the meaning and value of robotics research. They learn some of the technical skills necessary for research on the Building-Wide Intelligence project, and for participation in the RoboCup@Home team. On its own, it serves as a primer on the topics that it covers. As a two-course sequence, it provides exposure to performing research in a real laboratory with real robots. It also can serve as a preparation for long-term research projects on a volunteer basis, as a peer mentor, or as a member of the UT Austin Villa RoboCup@Home Team.

More details about BWI can be found at http://www.cs.utexas.edu/~larg/bwi_web/
More details about RoboCup@Home can be found at http://www.robocupathome.org/

Teaching Objectives

The following topics will be covered:

• Introductory C++ programming
• Robot Operating System (ROS)
• Basics of Artificial Intelligence & Human-Robot Interaction
• Reading & writing scientific research papers
• Reading research with a critical eye and understanding
• Giving good presentations
• Performing and understanding good research

Some of the assignments you undertake this semester will require you to use the BWI Lab, in GDC 3.414.

In order to maintain productivity in the lab, please observe the following. Failure to do so will harm the lab's productivity, and repeat offenders will be banned from the lab and receive failing grades on assignments taking place in the lab.

• If you break something, immediately report it to a mentor, the TA, or the instructor. (In that order.)
  • Breakages happen, but we have to handle them quickly as downtime can harm research progress.
  • Students and faculty performing research for scientific publications work on strict deadlines, and problems must be quickly addressed.
• Always leave the robots on chargers when not in use, regardless of whether you were the last to use them.
• Do NOT over-tighten the screws on the robot charging cables. The screws will break, and this will lead to downtime.
  • A red light means that the charger is not connected. Report issues to a mentor, the TA, or the instructor. (In that order.)
  • A flashing green light means that the charger in a special charging cycle. It's fine.
• Do NOT leave computers locked. Some computers may be locked by researchers in the lab. Students should not lock them.
• The computers with the LED cases or connected to robots are for student use (except the Alienware laptop, or Alienware gaming PC).
  • On the first day in lab, a few computers with special purposes will be identified. Do not log into these machines either locally or remotely.
• Do not hold group meetings in the hallways or meeting areas on the AI corridor with no door. Those areas are for the use of AI lab graduate students, faculty, and staff only.
• Do not meet with groups other than your robotics final project group in the lab. That space is reserved for class and research purposes.

There is no textbook for this course.

There will be a series of 4 papers to which students are expected to give written responses and participate in in-class discussions.

At the end of the semester, a final research project will be performed as groups. For this, students are expected to act on what they learned this semester and perform a literature survey to justify the ideas in their experiments.

Class sessions will be held in Welch (WEL) 2.128 on Tuesdays and Thursdays. Attendance is mandatory. Students are expected to email the instructor in advance to inform of any potential absences. Several of the homeworks will involve working on real robots, which can be found in the laboratory, GDC 3.414.

Students are also expected to be able to work independently. There is no programming pre-requisite for this course, though a working knowledge of programming will be helpful. Four of the six first lectures in the class will be dedicated to C++ programming, with intensive programming instruction in ROS to follow. Homeworks and projects will utilize ROS in the C++ programming language.

It is advisable (though not mandatory) to install ROS on a Linux machine (preferably a laptop) in order to do your homework and projects for this class.

Running ROS in a virtual machine tends to work very poorly, and the mentors, TA, and instructor will not help you accomplish this. We are happy to help you with ROS on native Linux machines.

Alternatively, the machines in the BWI lab and in the downstairs computer lab have ROS installed. Note that all of the robots in the BWI lab run ROS Kinetic, which is kept in sync with the Toyota Human Support Robot. The department supports Melodic. As such, there may be subtle differences.

Grades will be based on:

Class participation and attendance	10%
Reading Responses	10%
Homework	60%
Final Project	20%

The final project will comprise the following components. You will be graded equally on both of these as well as successful completion of the project:

Final Presentation

Final Project Report

Plus and minus grades will be used in final grading of the course.

Final project presentations will be during the final exam slot on Monday, May 20 from 9:00am-12:00pm.
Final project reports will be due on Monday, May 20 immediately following the final project presentation.

Schedule subject to change due to pace of class, see website for updates.
Reading responses due the night before corresponding reading discussions at 11:59pm.

Date	Title	Assignment Set	Assignment Due	Readings	Slides
01/22/19	Introduction, Kickoff with Peter Stone, Panel with Peer Mentors				[PDF]
01/24/19	Setting the Stage: An Introduction to Artificial Intelligence: Part 1				[PDF]
01/29/19	C++ Tutorial: Part 1				No Slides
01/31/19	Setting the Stage: An Introduction to Artificial Intelligence: Part 2				[PDF]
	C++ Tutorial: Part 2
02/05/19	C++ Tutorial: Part 3	HW1 goes out			No Slides
02/07/19	C++ Tutorial: Part 4				[PDF]
	Symbolic Reasoning, Search, & PDDL
02/12/19	Symbolic Reasoning, Search, & PDDL				[PDF]
02/14/19	Symbolic Reasoning, Search, & PDDL	HW1 due 11:59pm			[PDF]
	How to Read Papers			Paper 1: No Fair!! An Interaction with a Cheating Robot	[Paper PDF]
02/18/19				Reading Responses due 11:59pm
02/19/19	Paper 1: No Fair!! An Interaction with a Cheating Robot	HW2 goes out			[PDF]
	Intro to Human-Robot Interaction
02/21/19	Intro to ROS				[PDF]
02/26/19	Publish/Subscribe & ROS Topics				[PDF]
	ROS Service Tutorial
02/27/19			HW2 due 11:59pm
02/28/19	Publish/Subscribe & ROS Topics				[PDF - Continued from Last Time]
				Paper 2: Elephants Don't Play Chess	[Paper PDF]
03/04/19				Reading Responses due 11:59pm
03/05/19	Reading Discussion: Elephants Don't Play Chess				[PDF]
	ROS Topics & Service Calls
03/07/19	OpenCV and rviz				[PDF]
03/12/19	Coordinate Frames & Spatial Transformations	HW3 goes out			[PDF]
03/14/19	Starting the Robot				[PDF]
	Coordinate Frames & Spatial Transformations
	TF
03/19/19	SPRING BREAK
03/21/19	SPRING BREAK
03/26/19	Starting the Robot (Recap)				[PDF]
	TF (Recap)
	AR Tags
	Project Group Formation
03/28/19	BWIBot Demo		HW3 due 11:59pm
03/29/19		HW4 goes out
04/02/19	Alvar, TF, and Eigen				[PDF]
04/04/19	ROS Navigation Goals				[PDF]
04/09/19	LaTeX		Plan for a partial solution for HW5 by tonight.		[PDF]
	Final Project Proposals
	Getting Through HW5
04/11/19	Writing Final Papers				[PDF]
	Final Project Proposal Workshop				[PDF]
04/16/19	Final Project Proposal Debugging		HW6 due 11:59pm
04/18/19	Work Session in Lab
04/23/19	Work Session in Lab
04/25/19	Work Session in Lab
04/30/19	Work Session in Lab
05/02/19	Work Session in Lab
05/07/19	Work Session in Lab
05/09/19	Final Presentations				[PDF]
05/14/19	Dead Week
05/16/19	Dead Week
05/20/19	Final Project Presentations & Papers

• Assignments are subject to revision, possibly significant, up to the date assigned.
• You are free to take a look at the homework that is ahead.
  • Please do not skip class because you have gotten ahead, you may miss something that you need and it will harm your participation grade!
  • You are responsible for doing the homework as assigned on the day it is assigned. So, if you attempt a future assignment, please check back to make sure it has not been revised before turning it in!

Due date	Homework	Instructions	Files
02/12/18	HW 1: C++ Exercises	[PDF]	[DIR]
02/26/18	HW 2: Planning with PDDL	[PDF]
03/26/18	HW 3: ROS Basics & Simple Color Filtering	[PDF]	[BAG]
04/02/18	HW 4: Start up the Robot	[PDF]
04/11/18	HW 5: Follow the AR Tag	[PDF]	[Package in a .tar.gz]
04/16/18	HW 6: 1-Page Final Project Description (Based on Slides)
05/20/18	Final Project Paper
05/20/18	Final Project Presentation - During Exam Period

Software	Host Site	Notes
Ubuntu Linux	https://www.ubuntu.com/	You want 16.04
ROS	https://www.ros.org/	You want Kinetic, if possible
FF	https://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz	https://fai.cs.uni-saarland.de/hoffmann/ff.html

To be released as we cover this material in class.

Example	File	Description
All C++ examples covered in class as a tar.gz	[c++.tar.gz]
Example 01	[ex01]	Hello World! Using printf
Example 02	[ex02]	Hello World! Using std::cout
Example 03	[ex03]	Variables
Example 04	[ex04]	Assignment. Initialization. Pre/post-increment.
Example 05	[ex05]	Loops
Example 06	[ex06]	Functions
Example 07	[ex07]	Scoping
Example 08	[ex08]	Header files and function prototypes
Example 09	[ex09]	Header files with separate implementations
Example 10	[ex10]	Comparisons, if/then/else, basic flow control.
Example 11	[ex11]	Basic types.
Example 12	[ex12]	Pointers and references.
Example 13	[ex13]	Arrays.
Example 14	[ex14]	Vectors.
Example 15	[ex15]	Classes.
Example 16	[ex16]	Pulling it Together.
Example 17	[ex17]	Inheritance and Abstract Classes.
Example 18	[ex18]	Runtime errors & signed variables.

To be released as we cover this material in class.

Example	File	Description
All PDDL examples covered in class as a tar.gz	[pddl.tar.gz]
Example 01	[ex01]	Pick up the block
Example 02	[ex02]	Stack blocks
Example 03	[ex03]	Maze
Example 04	[ex04]	Door

To be released as we cover this material in class.

Example	File	Description
beginner_tutorials package as a tar.gz	[beginner_tutorials.tar.gz]
vision_tutorials package as a tar.gz	[vision_examples.tar.gz]

As this is a research course, it is important to use the many tools at your disposal to achieve your research goals. Students will work in groups in this course, and are expected to collaborate with their teams and outside of their immediate teams in order to achieve the best results possible. When you leverage someone else's work, cite them. Citations are the currency of the scientific community. Use third-party software, but make sure to honor licenses and cite the authors. In this course, you will be graded on what you accomplish above and beyond what is already freely available. If this means implementing an algorithm, state which parts were your original work or implementation in your progress reports, and which parts were downloaded or were someone else's ideas. In this class, leveraging such resources is encouraged. It makes code easier to maintain and update, and encourages potential collaborations with other institutions. Invest your efforts in making novel discoveries or implementing functionality beyond what is freely available. Do, however, abide by Computer Science Department's Academic Honesty Policy, which can be found at http://www.cs.utexas.edu/users/ear/CodeOfConduct.html

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. Further information can be found at http://www.utexas.edu/diversity/ddce/ssd/.

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, as presented by Justin Hart in conjunction with Peter Stone. It is part of the University of Texas at Austin, Texas Institute for Discovery Education in Science, Freshman Resaerch Initiative Program. The course was originally conceived by Peter Stone, and this material is an evolution of material developed by Jivko Sinapov, who succeeded Matteo Leonetti. It is influenced by Brian Scassellati's CS 473b: Intelligent Robotics course at Yale University.