CISC849 F2019

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Course information

Title CISC849 Robot Vision and Learning
Description Survey of image-based 2-D and 3-D sensing algorithms for mobile robot navigation and interaction, including motion estimation, obstacle segmentation, terrain modeling, and object recognition, with a particular focus on deep learning techniques to dramatically improve performance.
When Tuesdays and Thursdays, 2-3:15 pm
Where Gore 317
Instructor Christopher Rasmussen, 446 Smith Hall, cer@cis.udel.edu
Office hours Mondays and Wednesdays, 2-3 pm
Grading
  • 25% Oral paper presentation (individual, 30 minutes)
  • 25% Programming/learning assignment (individual)
  • 50% Final project (1 or 2 students)
    • 10% = 2 page proposal, including planned methods, citations of relevant papers, data sources, and division of labor
    • 10% = Joint 15-minute presentation on final results, with accompanying slides
    • 30% = Actual results and estimated effort, factoring in difficulty of problem tackled
Academic policies Programming projects are due by midnight of the deadline day (with a grace period of a few hours afterward...after sunrise is definitely late). A late homework is a 0 without a valid prior excuse. To give you a little flexibility, you have 6 "late days" to use on homeworks to extend the deadline by one day each without penalty. No more than three late days may be used per assignment. Late days will automatically be subtracted, but as a courtesy please notify the instructor in an e-mail of your intention to use late days before the deadline. See submission instructions below.

Students can discuss problems with one another in general terms, but must work independently on programming assignments. This also applies to online and printed resources: you may consult them as references (as long as you cite them), but the words and source code you turn in must be yours alone. The University's policies on academic dishonesty are set forth in the student code of conduct here.

Homeworks Assignment submissions should consist of a directory containing all code (your .cpp files, makefile, etc.), any output data generated (e.g., images, movies, etc.), and an explanation of your approach, what worked and didn't work, etc. contained in a separate text or HTML file. Do not submit executables or .o files, please! The directory you submit for each assignment should be packaged by tar'ing and gzip'ing it or just zip'ing it. The resulting file should be submitted through Canvas.

You may develop your C/C++ code in any fashion that is convenient--that is, with any compiler and operating system that you want. However, we will be grading your homework on a Linux system with a makefile, and so you must avoid OS- and hardware-specific functions and provide a makefile for us that will work (like one of the templates above).

Possible Papers to Present (not a complete list)


Schedule

Note: The blue squares in the "#" column below indicate Tuesdays.

# Date Topic Links/Readings/videos Assignments/slides
1 Aug. 27 Background slides
2 Aug. 29 Introduction to DARPA Urban Challenge (UC), Robotics Challenge (DRC) and algorithm components slides
3 Sep. 3 More about Grand Challenges, trail-following slides
4 Sep. 5 Traditional 2-D/3-D shape/object segmentation, part I

slides

5 Sep. 10
Register/add deadline
NO CLASS
6 Sep. 12 Traditional 2-D/3-D shape/object segmentation, part II slides
7 Sep. 17 Image classification background Stanford CS231n Image classification slides (6-61),
8 Sep. 19 More classification background Loss/optimization slides (8-10, 37-77, finish with web demo), Intro to NN (83-88)
9 Sep. 24 Introduction to convolutional neural networks Stanford CS231n Convolutional Neural Networks slides (15-63, 71-78), CNN architectures (8-36), Detection and Segmentation slides (15-27)
10 Sep. 26 Finish detection & segmentation background, introduction to TensorFlow library YOLOv2, Redmon and Farhadi (CVPR 2017) Stanford CS231n Detection and Segmentation slides (44-49, 53-85), deep learning software slides (21-33, 40-59)
TensorFlow code sample from Stanford lecture
11 Oct. 1 More on TensorFlow programming and detection/segmentation Homework assigned
Oct. 3 NO CLASS
Instructor away
Oct. 8 NO CLASS
Instructor away
12 Oct. 10 Perception for stepping "Learning Locomotion over Rough Terrain using Terrain Templates", M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, IROS 2009
13 Oct. 15 Imitation learning * A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots, Giusti et al., IEEE Robotics and Automation Letters, 2016 Berkeley Levine course IL slides (DAgger defined on slide 14); CMU Fragkiadaki IL slides (1-29)
14 Oct. 17 Reinforcement learning UCL Silver course: RL slides (7-42), RL video (15:40-21:20, 1:02:13-1:04:40 contains movies missing from PDF); Stanford deep-Q lecture slides (23-62); original deep-Q paper


Homework due

15 Oct. 22
Withdraw deadline
RL for locomotion DeepLoco highlights video (6:23)
16 Oct. 24 Instructor paper presentation
17 Oct. 29 Student paper presentation
18 Oct. 31 Student paper presentation
19 Nov. 5 Student paper presentations
20 Nov. 7 Student paper presentations
21 Nov. 12 Student paper presentation
22 Nov. 14 Student paper presentations
23 Nov. 19 Student paper presentation
24 Nov. 21 Instructor paper presentation
Nov. 26 NO CLASS
Thanksgiving break
Nov. 28 NO CLASS
Thanksgiving break
25 Dec. 3 Final project presentations part I
26 Dec. 5 Final project presentations part II