Difference between revisions of "CISC849 S2018"

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|<!--Localization-->Deep learning libraries, <br>detection & segmentation background
 
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|[https://arxiv.org/abs/1612.08242 YOLOv2, Redmon and Farhadi] (CVPR 2017)
 
|[https://arxiv.org/abs/1612.08242 YOLOv2, Redmon and Farhadi] (CVPR 2017)
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|<!--ETH localization lectures: [http://nameless.cis.udel.edu/class_data/849_s2018/ETH_Localization_I_2017.pdf 1] [http://nameless.cis.udel.edu/class_data/849_s2018/ETH_Localization_II_2017.pdf 2]--><!--[https://docs.google.com/presentation/d/1CTGsRG7yvJVfZyBfaC1ZpBoUJ3ALbLaYnMeYZDM1wRA/edit?usp=sharing slides]-->Stanford CS231n [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf Deep Learning software slides] (21-33, 40-59)<br>[http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf Detection and Segmentation slides] (44-49, 53-85, 89-95)HW #2<!--[[CISC849_S2014_HW2|HW #2]]-->
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|<!--ETH localization lectures: [http://nameless.cis.udel.edu/class_data/849_s2018/ETH_Localization_I_2017.pdf 1] [http://nameless.cis.udel.edu/class_data/849_s2018/ETH_Localization_II_2017.pdf 2]--><!--[https://docs.google.com/presentation/d/1CTGsRG7yvJVfZyBfaC1ZpBoUJ3ALbLaYnMeYZDM1wRA/edit?usp=sharing slides]-->Stanford CS231n [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf Deep Learning software slides] (21-33, 40-59)<br>[http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf Detection and Segmentation slides] (44-49, 53-85, 89-95)<br>HW #2<!--[[CISC849_S2014_HW2|HW #2]]-->
 
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Revision as of 07:21, 13 March 2018

Course information

Title CISC849 Robot Vision and Learning
Shortened URL https://goo.gl/ektJij
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, 11 am-12:15 pm
Where Smith 102A
Instructor Christopher Rasmussen, 446 Smith Hall, cer@cis.udel.edu
Office hours Wednesdays, 10 am -- 12 pm
Grading
  • 20% Oral paper presentation (individual or pairs, 30 minutes)
  • 30% Two programming assignments (individual)
  • 50% Final project (teams of 1-3)
    • 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)

Instructions for Homeworks

Software
Note
  • TensorFlow is available for multiple operating systems, but the processing demands can be high, especially if your computer does not have a new-ish Nvidia GPU
  • If you have significant problems installing/using PCL or TensorFlow on your own computer, I have a GPU workstation in my lab that can be used for selected training jobs. Contact me *after* you have determined that your own machine is insufficient

Schedule

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

# Date Topic Links/Readings/videos Assignments/slides
1 Feb. 6 Background slides
2 Feb. 8 Finish background slides
3 Feb. 13 Introduction to DARPA Urban Challenge (UC), Robotics Challenge (DRC) and algorithm components slides
4 Feb. 15 Introduction to ROS; PCL tutorial

ARGOS challenge overview (8:00)

slides
Sample PCL programs (these require ROS)
ETH ROS mini course (in particular: overview, RViz, TF), ETH case study "ANYmal at the ARGOS Challenge"

5 Feb. 20
Register/add deadline Feb. 19 		Plane/obstacle/object segmentation (3-D) RANSAC background HW #1
plane_fit.cpp (No ROS required)
6 Feb. 22 Finish plane segmentation, clustering, ICP slides
7 Feb. 27 Image classification background Stanford CS231n Image classification slides (6-61),
8 Mar. 1 NO CLASS
Instructor away
HW #1 due
9 Mar. 6 More classification background Loss/optimization slides (8-10, 37-77, finish with web demo), Intro to NN (83-88)
10 Mar. 8 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)
11 Mar. 13 Deep learning libraries,
detection & segmentation background 		YOLOv2, Redmon and Farhadi (CVPR 2017) Stanford CS231n Deep Learning software slides (21-33, 40-59)
Detection and Segmentation slides (44-49, 53-85, 89-95)
HW #2
12 Mar. 15 More on detection, tracking
13 Mar. 20
14 Mar. 22 Perception for stepping "Learning Locomotion over Rough Terrain using Terrain Templates", M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, IROS 2009 HW #2 due Friday, March 23
Mar. 27 NO CLASS
Spring break
Mar. 29 NO CLASS
Spring break
15 Apr. 3 Imitation and reinforcement learning; project kick-off Berkeley Levine course IL slides; UCL Silver course: RL slides, RL video (contains movies missing from PDF)
Paper presentation choice due
16 Apr. 5 More on IL, RL
17 Apr. 10

Withdraw deadline Apr. 9

Student paper presentations
18 Apr. 12 Student paper presentations Project proposal due Sunday, April 15
19 Apr. 17
 Student paper presentations
20 Apr. 19 Early project review; student paper presentation
21 Apr. 24 Student paper presentations
22 Apr. 26 Student paper presentations
23 May 1 Miscellaneous
24 May 3 Final project review
25 May 8 "Bonus" material
26 May 10 Miscellaneous
27 May 15 Final project presentations Final project due
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