Difference between revisions of "CISC849 S2018"
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| − | |[http://cs231n.stanford.edu/syllabus.html Stanford CS231n] [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture2.pdf Image classification slides] (6-61), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf loss/optimization slides] (8-10, 37- | + | |[http://cs231n.stanford.edu/syllabus.html Stanford CS231n] [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture2.pdf Image classification slides] (6-61), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf loss/optimization slides] (8-10, 37-76)<!--[https://docs.google.com/presentation/d/1r8SfI3s0FVIrDBYi3ab_h-kwCC7fIpFsMgsIXBSxBgc/edit?usp=sharing slides] |
(Normals exampled added [https://bitbucket.org/crasmuss/my_pcl_tutorial.git here])--> | (Normals exampled added [https://bitbucket.org/crasmuss/my_pcl_tutorial.git here])--> | ||
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Revision as of 10:50, 27 February 2018
Contents
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 |
|
| 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)
- Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours, Pinto and Gupta, ICRA 2016
- Learning real manipulation tasks from virtual demonstrations using LSTM, Rahmatizadeh et al., AAAI 2018
- A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots, Giusti et al., IEEE Robotics and Automation Letters, 2016
- Learning to Fly by Crashing, Gandhi, Pinto, and Gupta, IROS 2017
- End to End Learning for Self-Driving cars, Bojarski et al., 2016
- VoxNet: A 3D Convolutional Neural Network for real-time object recognition, Maturana and Scherer, IROS 2015
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, Qi, Su, Mo, and Guibas, CVPR 2017
- Real-Time Navigation in 3D Environments Based on Depth Camera Data, D. Maier, A. Hornung, and M. Bennewitz, Humanoids 2012
- Robotic Grasping of Novel Objects using Vision, A. Saxena, J. Driemeyer, A. Ng, IJRR 2008
- High fidelity day/night stereo mapping with vegetation and negative obstacle detection for vision-in-the-loop walking, M. Bajracharya et al., IROS 2013
Instructions for Homeworks
| Software |
|
| Note |
|
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 |
| 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), loss/optimization slides (8-10, 37-76) | |
| 8 | Mar. 1 | Introduction to convolutional neural networks | Stanford CS231n Convolutional Neural Networks slides HW #1 due | |
| 9 | Mar. 6 | Introduction to OpenCV (in ROS), deep learning libraries, detection & segmentation background |
Stanford CS231n Deep Learning software slides, Detection and Segmentation slides | |
| 10 | Mar. 8 | More on detection, tracking | YOLOv2, Redmon and Farhadi (CVPR 2017) | HW #2 |
| 11 | Mar. 13 | Localization | ETH localization lectures: 1 2 | |
| 12 | Mar. 15 | Particle filters and localization | Humanoid Robot Localization in Complex Indoor Environments, A. Hornung, K. Wurm, M. Bennewitz, IROS 2010 | Thrun particle filtering slides |
| 13 | Mar. 20 | Motion planning background | HW #2 due | |
| 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 | Paper presentation choice due Friday, March 23 |
| Mar. 27 | NO CLASS Spring break |
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| Mar. 29 | NO CLASS Spring break |
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| 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) | |
| 16 | Apr. 5 | More on IL, RL | ||
| 17 | Apr. 10 Withdraw deadline Apr. 9 |
Student paper presentations | Project proposal due | |
| 18 | Apr. 12 | Student paper presentations | ||
| 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 |
