CS 378: Autonomous Intelligent Robotics (FRI II) - Fall 2017

InstructorJustin Hart
Class TimesTuesday & Thursday - 3:30pm-5:00pm
ClassroomRLM 7.116
LaboratoryGDC 3.414
Course Syllabus[PDF]

Office Hours & Contact Info


TimeMonday & Friday - 4:00pm-5:00pm
OfficeGDC 3.402


Mentor NameEmailTuesdayWednesdayThursdayFriday
Kathryn E. Baldaufkathrynbaldauf@utexas.edu11:00am-12:00pm1:00pm-3:00pm
Ricardo Delfin Garciaricardo.delfin.garcia@gmail.com11:00am-12:30pm 11:00am-12:30pm
Nathan Johnnathanjohn@utexas.edu2:00pm-3:00pm2:00pm-3:00pm, 4:30pm-5:30pm
Ashay J. Lokhandeashaylok@utexas.edu2:00pm-3:00pm4:00pm-5:00pm1:00pm-2:00pm
Rishi Shahrishihahs@gmail.com12:00pm-1:00pm3:00pm-4:00pm10:00pm-11:00pm
Benjamin Singerbenjamin.z.singer@gmail.com9:00am-12:00pm
Nick Walkernickswalker@icloud.com4:00pm-5:00pm11:00am-1:00pm
Victoria Zhouvictoria.ch.zhou@gmail.com3:30pm-4:30pm10:00am-12:00pm


HRI FormatCan be found here.Direct link to template.Also discussed in the 9/07 slides.

Group Meeting Schedule

Tuesdays3:30pm-4:00pmLab meeting
Tuesdays4:00pm-4:15pmBridgette Krause, Madeleine Williams, Bonny Mahayan, Anna Wang
Tuesdays4:15pm-4:30pmMehrdad Darraji, Shivram Patel, Danyaal Ali, Jamin Goo
Tuesdays4:30pm-4:45pmMatthew Webb, Jamison Miles, Anjuli Goring, Christian Onuogu
Thursdays4:00pm-4:15pmJeffrey Huang, Kevin Sheng
Thursdays4:15pm-4:30pmRaychel Beasley, Sam Gunn, Stone Tejeda, Mayuri Raja
Thursdays4:30pm-4:45pmTiffany Valitis

Class Schedule

08/31First Class!RLM 7.116Slides: [PDF]
09/05Team Formation & Project BrainstormingRLM 7.116Slides: [PDF]
09/07What does a good research presentation look like?
What does a good research report look like?
RLM 7.116Slides: [PDF]
09/12Preliminary Project Proposal PresentationsRLM 7.116
09/14Debug project proposalsGDC 3.414
09/19 & 09/21Work on proposals. Mentors available to help.GDC 3.414
09/25Project proposal reports due at 11:59pm.
09/26 & 09/28Project Proposal PresentationsRLM 7.116
10/03 and afterWork in lab on projectsGDC 3.414
12/11Final project reports due by 11:59pm.
12/16Final project presentations 2:00pm-5:00pmRLM 7.116


08/31 E. Short, J. W. Hart, M. Vu, and B. Scassellati. No Fair!! An Interaction with a Cheating Robot.
In Proceeding of the 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2010). Osaka, Japan, March 2010.
09/07 G. Whitesides. Whitesides' Group: Writing a Paper.
In Advanced Materials. vol. 16(15), p. 1375-1377

Course Description

This course focuses on expanding on what was learned in the first semester of FRI by diving more deeply into a directed project chosen jointly by student project teams and the instructor. Students participate as researchers in a real laboratory. As such, the challenges presented differ greatly from those of a typical undergraduate university course. Successful projects will make significant contributions to the laboratory; either by completing novel research or laying the groundwork for longer-term projects, or by developing important infrastructure components which support the laboratory's research efforts.

There are two significant research efforts upon which students are encouraged to focus their contributions. The first, the Building-Wide Intelligence (BWI) Project, aims to develop a fleet of service robots with which people in the Gates-Dell Complex may interact. These robots are intended to carry out useful and fun tasks and to become an integral part of the building's environment. The second is the development of software and studies to support the UT Austin Villa @ Home RoboCup@Home team. RoboCup@Home is a competition in which domestic service robots are tested on a series of challenge tasks. Robots perform a variety of tasks; such as helping people carry groceries into their apartment from a car, or acting on verbal instructions. 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/.

Philosophy & Goal

The goal of this class is to expose students to real experience researching as a robotics researcher. The challenges in this class are real. Unlike other classes in the undergraduate curriculum, the criteria for success are dictated by the needs of the laboratory and the state of the art of the field. This is a novel challenge for undergraduates, who must put themselves into the mindset of a doctoral student. The instructor and mentors will not know all of the answers to your scientific questions or how to implement all of your ideas. We will not know how easy or hard it is to accomplish what you are trying to do. We will provide support and advice on how to find the answers that you seek, and the result will be actual advancement of scientific knowledge in the fields of robotics, artificial intelligence, and human-robot interaction or the development of necessary infrastructure to support our research projects.
Students will:


There is no textbook for this course. Instead, students will perform a literature survey; finding papers relevant to their research project. Grading of these surveys will follow the criteria that peer reviewers use when performing the scientific peer review process. These papers should explore prior work on the problem that the student team is investigating, demonstrate an understanding of the problem or phenomenon, and support the investigation that the students perform. Much like the literature survey in an archived scientific paper, a successful literature survey both demonstrates that the students understand the current state of the art and supports the hypothesis tested or approach taken in their research. Students may also be assigned readings as part of class exercises, and will be expected to read the paper and possibly compose a written response to be emailed to the instructor prior to in-class discussion.


This class will incorporate a mixture of classroom instruction and laboratory practice. Class sessions will be held in RLM 7.116. The laboratory can be found at GDC 3.414. The first class will be held in RLM 7.116. On days when class is not held in RLM 7.116, students are expected to come to the laboratory in GDC 3.414. An up-to-date schedule of class locations and dates can be found on the course website.

During laboratory days, student teams will have scheduled meeting slots with the instructor in which progress will be evaluated and general guidance will be provided. These meetings will take a format similar to the regular weekly meetings that a PhD student would have with their doctoral advisor.


Participation in either FRI I or the Summer ARI program is a prerequisite for this course. Students are also expected to be able to work independently and in teams on projects utilizing ROS in either the C++ or Python programming languages. When choosing course projects, students should carefully consider the prerequisite or corequisite knowledge necessary for their success, as this will vary from project to project.

Choosing a Project

Early in the course, project ideas will be offered by the instructor. These project ideas are intended to seed discussion and may either become the project that a student team pursues or the inspiration for their project. Student teams may also come up with ideas all their own. Successful projects will balance ambition with pragmatism and make a real contribution to the lab. Projects will be mutually agreed upon by student teams and the instructor, and the instructor will provide guidance in the development of these ideas.


Grades will be based on:
Class participation and attendance (including reading responses) 20%
Final Project 80%

The final project will comprise the following components:
Project Proposal, Writeup, & Presentation 30%
Progress Report 1 10%
Progress Report 2 10%
Final Project Report 25%
Final Project Presentation 25%

Optionally, the final project can include a live demonstration, yielding the following breakdown:
Project Proposal, Writeup, & Presentation 30%
Progress Report 1 10%
Progress Report 2 10%
Final Project Demonstration 10%
Final Project Report 20%
Final Project Presentation 20%

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

Final project reports will be due on Monday, December 11 by 11:59pm.
Final project presentations will be during the final exam slot on Saturday, December 16 from 2:00-5:00pm.

Academic Integrity

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\#honesty

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

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, as presented by Justin Hart, 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. It has been developed in conjunction with Peter Stone.