Office hours [top]
Instructor:
Time: by
appointment
Office: GDC 3.432
Email:
jsinapov--AT--cs--DOT--utexas--DOT--edu
Teaching assistants and Mentors:
The TAs and the mentors can meet you in the lab, GDC 3.414b.
TA
Ricardo Delfin Garcia | ricardo.delfin.garcia@gmail | M and W | 2:00 - 4:00 pm |
T and Th | 1:00 - 2:00 pm | ||
Yuqian Jiang | jiangyuqian@utexas | M | 1:00 - 2:00 pm |
T and Th | 2:00 - 3:30 pm | ||
F | 1:00 - 2:00 pm | ||
F | 3:00 - 4:00 pm |
Mentors
Kathryn Baldauf | kathrynbaldauf@utexas | M | 1:00 - 3:00 pm |
T | 2:00 - 3:00 pm | ||
Ashay Lokhande | ashaylok@utexas | T | 2:00 - 3:00 pm |
W | 10:00 - 1:00 pm | ||
Palakkumar Hirpara | palak_hirpara@yahoo | T | 11:00 - 1:00 pm |
W | 1:00 - 2:00 pm | ||
Nathan John | nathanjohn@utexas | Th | 9:00 - 11:00 am |
F | 11:00 am - noon | ||
Saket Sadani | saketsadani@utexas | M | 10:00 am - noon |
F | 11:00 - noon | ||
Walter Sagehorn | wsagehorn@gmail | M | 2:00 - 4:00 pm |
F | 2:00 - 3:00 pm | ||
Benjamin Singer | benjamin.z.singer@utexas | W | 10:00 - 11:00 am |
W | noon - 4:00 pm | ||
Aylish Wrench | aylish.wrench11@gmail | M | 2:00 - 4:00 pm |
W | 12:00 - 1:00 pm | ||
Victoria Zhou | victoria.ch.zhou@gmail | M | 10:00 am - noon |
W | 12:30 pm - 1:30 pm | ||
Rishi Shah | rishihahs@gmail | T and Th | 2:00 - 3:30 pm |
Nick Walker | nickswalker@icloud | M and W | 11:30 - 1:00 pm |
Final Projects [top]
Final Project Proposal: Due Apr 3
Final Project Presentations: May 13 7:00 pm - 10:00 pm [SCHEDULE]
Final Project Deliverables: Due May 15
- Title: Implementation of 3D Object Recognition Based on Correspondence Grouping
Team: Jamison Miles, Kevin Sheng, and Jeffrey Huang
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Leading a Visitor Intelligently
Team: Luke Wright, Stone Tejeda, and Katherine Hackworth
Proposal: [PDF] Final Report: [PDF] Code: [Github] Videos: [1] [2] - Title: Autonomous Simultaneous Localization And Mapping
Team: Michail Shaposhnikov, Jake Crabtree, and Mustafa Abban
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Make Way Please
Team: Bonny Mahajan and Anna Wang
Proposal: [PDF] Final Report: [PDF] Code: [Github] Videos: [1] [2] - Title: Robot Communication through Text-to-Speech
Team: Jordan Taylor and Christian Onuogu
Proposal: [PDF] Final Report: [PDF] Code: [Github] Videos: [1] [2] [3] - Title: Multi-Step Vocal Command
Team: Edward DeCoste and Andrea Youwakim
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Robot Collision Avoidance
Team: Brigette Krause and Madeleine Williams
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Emergency Stop Button Proposal
Team: Yujie (Jessie) Chen and Jeremy Cook
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Autonomous Localization
Team: Maya Kothare-Arora and Jennifer (Yuanhui) Zheng
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Using Hand Gesture To Command BWI Segbots
Team: Mehrdad Darraji and Jamin Goo
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Creating a Web Application to Handle Battery Scheduling
Team: Tiffany Valitis and Raychel Beasley
Proposal: [PDF] Final Report: [PDF] Code: [Github] - Title: Learning Names through Facial Recognition
Team: Ailyn Aguirre, Anjuli Goring and Matthew Webb
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Lighting the Way to Safety
Team: Danyaal Ali, Shivam Patel and Frank Valdez
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1] - Title: Environment-based Music Generation
Team: Smitha Nagar, Mayuri Raja, and Xinyu Zhao
Proposal: [PDF] Final Report: [PDF] Code: [Github] Video: [1]
Class Diary (including links to slides and readings) [top]
- 5/2 and 5/4 - Project Breakout
Slides from class: [PDF] - 4/27 - Introduction to Reinforcement Learning (II)
Slides from class: [PDF] - 4/25 - Introduction to Reinforcement Learning
Slides from class: [PDF] - 4/20 - High-level Robot Goals Example
Slides from class: [PDF] - 4/18 - Machine Learning Introduction
Slides from class: [PDF] - 4/13- Reading Discussion
Slides from class: [PDF]
Assigned Reading (Chapter 1): [1] - 4/11- Positions and Orientations in 3D Space
Slides from class: [PDF]
- 4/6 - 3D Vision: Introduction to Point Cloud Library
Slides from class: [PDF]
PCL code from class: [TAR.GZ] - 4/4 - Computer Vision: Motion
Slides from class: [PDF]
Assigned Readings: [1] [2] [3]
- 3/23 - Breakout
Slides from class: [PDF]
- 3/21 - Introduction to Computer Vision
Slides from class: [PDF]
Homework 5 (due March 31st): [TXT]
move_base_client code from class: [ZIP]
computer vision code from class: [TAR.GZ] - 3/9 Reading Discussion + Project Proposal Details
Slides from class: [PDF]
Assigned Readings (Computer Vision): [1] [2] [3]
- 3/7 - Robot bodies in ROS
Homework 4 (due March 22nd): [TXT]
- 3/2 - Where in the world is the robot? (aka localization and mapping)
Slides from class: [PDF] - 2/28 - Robot Training Session (anytime during the week)
- 2/23 - Introduction to our code base and simulation environment
- 2/21 - Planning and Behavior-Based Robotics
Homework 3 (due March 6th): [TAR.GZ]
Assigned Readings: [1] [2] [3]
- 2/16 - Reading Discussion and HW2 Q & A
Slides from class: [PDF] - 2/9 - Git and github tutorial
- 2/7 - Introduction to ROS (III)
Homework 2 (due Feb 20th): [TAR.GZ]
Assigned Reading: [1] - 2/2 - Introduction to ROS (II)
Slides from class: [PDF]
Code from class: [TAR.GZ]
- 1/31 - Introduction to ROS
Slides from class: [PDF]
Assigned Readings: [1] [2] [3]
- 1/26 - Introduction to C++ (part 3)
Slides from class: [PDF]
Code from class: [TAR.GZ]
Homework 1 (due Feb 6th): [TAR.GZ] - 1/24 - Introduction to C++ (part 2)
Slides from class: [PDF] - 1/19 - Introduction to C++
Slides from class: [PDF]
Hello World cpp: [TAR.GZ] - 1/17 - Class Introduction
Assigned Readings: [1] [2] [3]
Slides from class: [PDF]
Course Overview [top]
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 [top]
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.
Course Requirements [top]
Grades will be based on
- class participation (10%);
- written responses to the readings; (10%)
- preliminary assignments; (60%)
- a final programming project. (20%)
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.
Prerequisites [top]
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 [top]
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 [top]
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 [top]
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 [top]
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.
Credits [top]
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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