Difference between revisions of "CISC849 F2019"

Latest revision as of 20:59, 2 December 2019

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 (1-3 students) 50% Final project (1-3 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)

Where Should I Walk? Predicting Terrain Properties from Images via Self-Supervised Learning, Wellhausen et al., IEEE Robotics and Automation Letters, 2019
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
Learning to Fly by Crashing, Gandhi, Pinto, and Gupta, IROS 2017
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
DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning, X. Peng, G. Berseth, K. Yin, and M. van de Panne, SIGGRAPH 2017
High fidelity day/night stereo mapping with vegetation and negative obstacle detection for vision-in-the-loop walking, M. Bajracharya et al., IROS 2013

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	"Crashes and Traffic Jams in Military Test of Robotic Vehicles" (NYTimes, Nov. 5, 2007) UC highlights (6:06), Stanford UC clips DRC highlights (5:47) NOVA "Rise of the Robots"	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	NO CLASS
9	Sep. 24	More classification background; Introduction to convolutional neural networks		Stanford CS231n: Loss/optimization slides (8-10, 37-77, finish with web demo), Intro to NN (83-88), Convolutional Neural Networks slides (15-63, 71-78), CNN architectures (8-36)
10	Sep. 26	Detection & segmentation background	YOLOv2, Redmon and Farhadi (CVPR 2017)	Stanford CS231n Detection and Segmentation slides (15-27, 44-85, 89-94)
11	Oct. 1	TensorFlow programming and homework		Deep learning software slides (21-33, 40-59) my slides 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	ANYmal video, ANYmal slides, ANYmal "Where Should I Walk?" video, Kalakrishnan slides
13	Oct. 15	NO CLASS Instructor away
14	Oct. 17	Imitation learning	Agile Autonomous Driving using End-to-End Deep Imitation Learning, Pan et al., RSS 2018 A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots, Giusti et al., IEEE Robotics and Automation Letters, 2016 End to End Learning for Self-Driving cars, Bojarski et al., 2016 Quadcopter Navigation in the Forest..., DAgger for drones Hal Daume DAgger explanation (9:26-12:43)	Berkeley Levine course IL slides (DAgger defined on slide 14); CMU Fragkiadaki IL slides (1-29) Homework due
15	Oct. 22 Withdraw deadline	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
16	Oct. 24	NO CLASS Break before presentations start
17	Oct. 29	Student paper presentation	Maxim Bazik, "PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud"
18	Oct. 31	Student paper presentation	Woosik Lee, "Learning Wheel Odometry and IMU Errors for Localization"
19	Nov. 5	Student paper presentations	Yi Liu, "Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation"
20	Nov. 7	Student paper presentations	Chuchu Chen, "Deep Learning for 2D Scan Matching and Loop Closure" Chirantan Ghosh, "Video Action Transformer Network"
21	Nov. 12	Student paper presentation	Sha Liu, "Robots that can adapt like animals" Seyedalireza Khoshsirat, "PCN: Point Completion Network"
22	Nov. 14	Student paper presentations	Shengye Li, "Learning Common and Specific Features for RGB-D Semantic Segmentaion with Deconvolutional Networks" Eric Wright, "Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight"
23	Nov. 19	Student paper presentation	Shivanand Sheshappanavar, "PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows" Zhang Guo, "Believe It or Not, We Know What You Are Looking at!"
24	Nov. 21	Student paper presentations	Nikhil Gothankar, "Fast Online Object Tracking and Segmentation: A Unifying Approach" Vineet Singh, "Spatial Transformer Networks"
	Nov. 26	NO CLASS Thanksgiving break
	Nov. 28	NO CLASS Thanksgiving break
25	Dec. 3	Student paper presentation	Md Mottalib, "An Empirical Evaluation of Deep Learning on Highway Driving"
26	Dec. 5, 6	Final project presentations

Difference between revisions of "CISC849 F2019"

Latest revision as of 20:59, 2 December 2019

Course information

Possible Papers to Present (not a complete list)

Schedule

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@@ Line 25: / Line 25: @@
 |valign="top"|'''Grading'''
 |
-* 20% Oral paper presentation (individual or pairs, 30 minutes)
+* 25% Oral paper presentation (individual, 30 minutes)
-* 30% Two programming assignments (individual)
+* 25% Programming/learning assignment (1-3 students)
-* 50% Final project (teams of 1-3)
+* 50% Final project (1-3 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
@@ Line 36: / Line 36: @@
 |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 [http://www1.udel.edu/stuguide/17-18/code.html here].
+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 [http://www1.udel.edu/stuguide/19-20/code.html here].
 |-
 |valign="top"|'''Homeworks'''
@@ Line 46: / Line 46: @@
 ==Possible Papers to Present (not a complete list)==
+* [https://www.research-collection.ethz.ch/handle/20.500.11850/323783 Where Should I Walk? Predicting Terrain Properties from Images via Self-Supervised Learning], Wellhausen ''et al.'', ''IEEE Robotics and Automation Letters'', 2019
 * [https://arxiv.org/pdf/1509.06825.pdf Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours], Pinto and Gupta, ICRA 2016
 * [http://www.cs.ucf.edu/~rrahmati/manipulationLearning.html Learning real manipulation tasks from virtual demonstrations using LSTM], Rahmatizadeh ''et al.'', AAAI 2018
@@ Line 63: / Line 64: @@
 * Detection/recognition-->
+<!--
 ==Instructions for Homeworks==
 {| class="wikitable" border="0" cellpadding="5"
@@ Line 74: / Line 76: @@
 ** [http://www.pointclouds.org/documentation/ Documentation]
 ** [https://larrylisky.com/2016/11/03/point-cloud-library-on-ubuntu-16-04-lts/ Installation on Ubuntu 16.04] (including prerequisite libraries)
-<!--
-* [http://www.ros.org/ ROS]
-**[http://www.ros.org/wiki/ROS/Installation Installation instructions] (Kinetic, Ubuntu 16.04, Desktop-Full Install).  It includes [http://pointclouds.org/ PCL] for 3-D point cloud processing and [http://opencv.org/ OpenCV] for computer vision/image processing.  We will mainly be using ROS for these included libraries and the visualization functionality of the rviz tool, so don't worry about "learning" ROS.  If you're curious, links to more information are below.
-** [http://clearpath.wpengine.netdna-cdn.com/wp-content/uploads/2014/01/ROS-Cheat-Sheet-Landscape-v2.pdf "Cheatsheet"]
-** [http://www.ros.org/wiki/roblab-whge-ros-pkg Tutorial videos]
-** [http://www.ros.org/wiki/rviz/UserGuide Rviz user guide]
-* [https://www.tensorflow.org/ TensorFlow]
-** [https://www.tensorflow.org/install/ Installation instructions]
--->
 |-
 |valign="top"|'''Note'''
 |
-<!--
-* ROS works best with Linux.  If you don't have a Linux distribution running currently, it's not hard to add one to your machine.  The [http://www.ubuntu.com Ubuntu website] has comprehensive instructions on installing Ubuntu Linux from different sources (CD-ROM, USB stick, etc.) on a separate partition (aka "dual booting"), or you can use a virtual machine if you are comfortable with that.  I recommend version [https://www.ubuntu.com/download/desktop 16.04.3 LTS].
--->
 * 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
-<!--
+|}
-* PCL
-** Do not install separately--we will just use version (1.7) included in ROS Kinetic
-* OpenCV
-** Do not install separately--we will just use version (3) included in ROS Kinetic
 -->
-|}
 ==Schedule==
@@ Line 115: / Line 101: @@
 |Background
 |
-|[https://docs.google.com/presentation/d/13tXZozuTBfFBte2vjSRkWXHlVJv0yBUDonySzBULUDI/edit?usp=sharing slides]
+|[https://docs.google.com/presentation/d/1SRF2C1JlcfIh-eNwXQn2WLs3qvumT91bmPOLIvk1sfY/edit?usp=sharing slides]
 |-
 |2
 |Aug. 29
-|Finish background
-|
-|[https://docs.google.com/presentation/d/1aEWKMVET3Hnn5eCg-vX9Li2hSMPD7kpgGZSAMNUh-cM/edit?usp=sharing slides]
-|-
-|style="background:rgb(102, 204, 255)"|3
-|Sep. 3
 |Introduction to DARPA Urban Challenge (UC), Robotics Challenge (DRC) and algorithm components
 |
@@ Line 131: / Line 111: @@
 * [https://www.youtube.com/watch?v=8P9geWwi9e0 DRC highlights] (5:47)
 * [http://www.pbs.org/wgbh/nova/tech/rise-of-the-robots.html NOVA "Rise of the Robots"]
-|[https://docs.google.com/presentation/d/1gziYFt4rrb6tZ9fKm_oJ0tTHPHPX5LvKWcUo0k1mNBM/edit?usp=sharing slides]
+|[https://docs.google.com/presentation/d/1u3Ud37TiMIzCZiERMBb7_QUIrq4hsR_yOWtFGLlgNCA/edit?usp=sharing slides]
+|-
+|style="background:rgb(102, 204, 255)"|3
+|Sep. 3
+|More about Grand Challenges, trail-following
+|
+|[https://docs.google.com/presentation/d/1fxzYKVm8018leCbFE0VtYG1_oRWs6_sXpQww_Cw6ztQ/edit?usp=sharing slides]
 |-
 |4
 |Sep. 5
-|Introduction to ROS; PCL tutorial
+|Traditional 2-D/3-D shape/object segmentation, part I
+|<!--
+[https://www.youtube.com/watch?v=kdx-DFI1VuA ARGOS challenge overview] (8:00)-->
 |
-[https://www.youtube.com/watch?v=kdx-DFI1VuA ARGOS challenge overview] (8:00)
+[https://docs.google.com/presentation/d/1rwh5lStdPV1EWdynRA18m5_yGMVMfldZFdHOdWJGeFg/edit?usp=sharing slides]<!--<br>
-|
-[https://docs.google.com/presentation/d/1fTDJkK5XwvzuF3pPr3I7CHjG4lhqpDCOulPDamjcekM/edit?usp=sharing slides]<br>
 [https://bitbucket.org/crasmuss/my_pcl_tutorial.git Sample PCL programs] (these require ROS)<br>[http://www.rsl.ethz.ch/education-students/lectures/ros.html ETH ROS mini course] (in particular: overview, RViz, TF), [http://nameless.cis.udel.edu/class_data/849_s2018/ETH_ANYmal_2016.pdf ETH case study "ANYmal at the ARGOS Challenge"]
+-->
 |-
 |style="background:rgb(102, 204, 255)"|5
 |Sep. 10<br>''Register/add deadline''
-|Plane/obstacle/object segmentation (3-D)
+|style="background:rgb(255, 102, 0)"|NO CLASS
-|[https://en.wikipedia.org/wiki/Random_sample_consensus RANSAC background]
+|<!--[https://en.wikipedia.org/wiki/Random_sample_consensus RANSAC background]-->
-|[[CISC849_S2018_HW1|HW #1]]<br>[https://drive.google.com/open?id=1NcSmS-plATqoWhuv4-BSjQ5xept3ajOR plane_fit.cpp] (No ROS required)
+|<!--[[CISC849_S2018_HW1|HW #1]]<br>[https://drive.google.com/open?id=1NcSmS-plATqoWhuv4-BSjQ5xept3ajOR plane_fit.cpp] (No ROS required)-->
 |-
 |6
 |Sep. 12
-|Finish plane segmentation, clustering, ICP
+|Traditional 2-D/3-D shape/object segmentation, part II
 |<!--"Autonomous Door Opening and Plugging In with a Personal Robot", W. Meeussen ''et al.'', IROS 2010<br>"Biped Navigation in Rough Environments using On-board Sensing", J. Chestnutt, Y. Takaoka, K. Suga, K. Nishiwaki, J. Kuffner, and S. Kagami, IROS 2009-->
-|[https://docs.google.com/presentation/d/17D8zMyRES-0sN7OeW28V0xFLqVOE4pFvjscs6eVEzL0/edit?usp=sharing slides]<!--
+|[https://docs.google.com/presentation/d/1nQ7mG2fbxH_HzHyriiiQ90zTmYmw-RlsRiep7cGOHsg/edit?usp=sharing slides]<!--
 (Clustering example added [https://bitbucket.org/crasmuss/my_pcl_tutorial.git here])-->
 |-
@@ Line 164: / Line 151: @@
 |8
 |Sep. 19
-|More classification background
+|style="background:rgb(255, 102, 0)"|NO CLASS<!--More classification background-->
 |
-|[http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf Loss/optimization slides] (8-10, 37-77, finish with web demo), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture4.pdf Intro to NN] (83-88)<br>''HW #1 due''
+|<!--[http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf Loss/optimization slides] (7-10, 37-77, finish with web demo), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture4.pdf Intro to NN] (83-88)-->
 |-
 |style="background:rgb(102, 204, 255)"|9
 |Sep. 24
-|Introduction to convolutional neural networks
+|More classification background; Introduction to convolutional neural networks
-|Stanford CS231n [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture5.pdf Convolutional Neural Networks slides] (15-63, 71-78), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture9.pdf CNN architectures] (8-36), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf Detection and Segmentation slides] (15-27)
+|
-|<!--[https://docs.google.com/presentation/d/1MrC0XyhFf-Q0kHgy46hcWyMXI7mpEiJxqXMVlfiX2CU/edit?usp=sharing&authkey=COWqkvYK slides]-->
+|Stanford CS231n: [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf Loss/optimization slides] (8-10, 37-77, finish with web demo), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture4.pdf Intro to NN] (83-88), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture5.pdf Convolutional Neural Networks slides] (15-63, 71-78), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture9.pdf CNN architectures] (8-36)<!--[https://docs.google.com/presentation/d/1MrC0XyhFf-Q0kHgy46hcWyMXI7mpEiJxqXMVlfiX2CU/edit?usp=sharing&authkey=COWqkvYK slides]-->
 |-
 |10
 |Sep. 26
-|Finish detection & segmentation background, introduction to TensorFlow library<!--Object recognition (sample paper)-->
+|Detection & segmentation background<!--Object recognition (sample paper)-->
 |[https://arxiv.org/abs/1612.08242 YOLOv2, Redmon and Farhadi] (CVPR 2017)<!--[http://research.microsoft.com/pubs/145347/bodypartrecognition.pdf "Real-Time Human Pose Recognition in Parts from Single Depth Images"], J. Shotton ''et al.'', CVPR 2011.  ''Recognition, classification, RGB-D''-->
-|Stanford CS231n [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf Detection and Segmentation slides] (44-49, 53-85), [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf deep learning software slides] (21-33, 40-59)<br>[https://drive.google.com/file/d/104GFBl4J6bseaTNBcqW2ub8hyXtL99d1/view?usp=sharing TensorFlow code sample from Stanford lecture]
+|Stanford CS231n [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf Detection and Segmentation slides] (15-27, 44-85, 89-94)
 |-
 |style="background:rgb(102, 204, 255)"|11
 |Oct. 1
-|More on TensorFlow programming<!--Localization-->
+|TensorFlow programming and homework<!--Localization-->
 |
-|<!--ETH localization lectures: [http://nameless.cis.udel.edAishwarya Dangu/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]-->[[CISC849_S2018_HW2|HW #2]]
+|[http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf Deep learning software slides] (21-33, 40-59)<br>[https://docs.google.com/presentation/d/1LzYpF66bT-LGEAifoDFmU-fPznlXWaKm0TnNKFXDWvM/edit?usp=sharing my slides] <!--ETH localization lectures: [http://nameless.cis.udel.edAishwarya Dangu/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]--><br>[[CISC849_F2019_HW|Homework assigned]]
 |-
-|12
+|
-|Oct. 8
+|Oct. 3
 |style="background:rgb(255, 102, 0)"|NO CLASS<br>''Instructor away''<!--Particle filters and localization-->
 |<!--[http://www2.informatik.uni-freiburg.de/~wurm/papers/hornung10iros.pdf Humanoid Robot Localization in Complex Indoor Environments], A. Hornung, K. Wurm, M. Bennewitz, IROS 2010-->
@@ Line 193: / Line 180: @@
 |-
 |style="background:rgb(102, 204, 255)"|
+|Oct. 8
+|style="background:rgb(255, 102, 0)"|NO CLASS<br>''Instructor away''<!--Motion planning background-->
+|
+|<!--[https://docs.google.com/presentation/d/1HQdeZ2JjATrWN7Y7BIgpeiFoIJa0J44P2d0bN_YSwRY/edit?usp=sharing slides]<br>-->
+|-
+|12
 |Oct. 10
-|<!--Motion planning background-->Perception for stepping
+|Perception for stepping
-|"Learning Locomotion over Rough Terrain using Terrain Templates", M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, IROS 2009
+|[https://ieeexplore.ieee.org/abstract/document/5354701 "Learning Locomotion over Rough Terrain using Terrain Templates"], M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, IROS 2009
-|[https://docs.google.com/presentation/d/1HQdeZ2JjATrWN7Y7BIgpeiFoIJa0J44P2d0bN_YSwRY/edit?usp=sharing slides]<br>[https://www.youtube.com/watch?v=G4lT9CLyCNw DeepLoco highlights video] (6:23)
+|[https://www.youtube.com/watch?v=DzTvIPrt0DY ANYmal video], [http://nameless.cis.udel.edu/class_data/849_s2018/ETH_ANYmal_2016.pdf ANYmal slides], [https://www.youtube.com/watch?v=TBppX3Ba_f0 ANYmal "Where Should I Walk?" video], [https://docs.google.com/presentation/d/14df-g9ph_W0t1ieMowREp1RH_a6BnvTtR7cmkEe8g8A/edit?usp=sharing Kalakrishnan slides]
 |-
-|14
+|style="background:rgb(102, 204, 255)"|13
 |Oct. 15
-|style="background:rgb(255, 102, 0)"|NO CLASS<!--Mapping-->
+|style="background:rgb(255, 102, 0)"|NO CLASS<br>''Instructor away''
-|<!--[http://wiki.ros.org/gmapping gmapping] demos: [http://www.youtube.com/watch?v=7iIDdvCXIFM Pirobot], [http://www.youtube.com/watch?v=_jwBKo0SXng MIT]<br>[http://www.youtube.com/watch?v=F8pdObV_df4 Darmstadt "Hector" mapping]<br>
-Nao 3-D mapping and planning: [http://www.youtube.com/watch?v=srcx7lPoIfw 1], [http://www.youtube.com/watch?v=g2NZ_EasJv0 2]-->
-|<!--ETH SLAM lectures: [http://nameless.cis.udel.edu/class_data/849_s2018/ETH_SLAM_I_2017.pdf 1] [http://nameless.cis.udel.edu/class_data/849_s2018/ETH_SLAM_II_2017.pdf 2]<!--[http://nameless.cis.udel.edu/class_data/cisc829/oct18/thrun_fastslam.pdf Thrun FastSLAM slides] (grids from slide 29)<br>[http://www.youtube.com/watch?v=3Yl2aq28LFQ Accompanying Stachniss lecture]-->''HW #2 due Friday, March 23''
-|-
-|style="background:rgb(102, 204, 255)"|
-|Oct. 17
-|
 |
 |
 |-
-|
+|14
-|Oct. 22<br>''Withdraw deadline''
+|Oct. 17
-|
-|
-|
-|-
-|style="background:rgb(102, 204, 255)"|15
-|Oct. 24
 |Imitation learning
 |
+* [https://www.youtube.com/watch?v=hUoDNeZS4so Agile Autonomous Driving using End-to-End Deep Imitation Learning], Pan ''et al.'', RSS 2018
 * [http://rpg.ifi.uzh.ch/docs/RAL16_Giusti.pdf A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots], Giusti ''et al.'', ''IEEE Robotics and Automation Letters'', 2016
 * [https://arxiv.org/pdf/1604.07316.pdf End to End Learning for Self-Driving cars], Bojarski ''et al.'', 2016
-* [https://www.youtube.com/watch?v=NJU9ULQUwng Dave-2 driving a Lincoln], [https://www.youtube.com/watch?v=umRdt3zGgpU Quadcopter Navigation in the Forest...], [https://www.youtube.com/watch?v=hNsP6-K3Hn4 DAgger for drones]
+* <!--[https://www.youtube.com/watch?v=NJU9ULQUwng Dave-2 driving a Lincoln], -->[https://www.youtube.com/watch?v=umRdt3zGgpU Quadcopter Navigation in the Forest...], [https://www.youtube.com/watch?v=hNsP6-K3Hn4 DAgger for drones]
 * [https://www.youtube.com/watch?v=ZMhO1FO_j0o&t=566s Hal Daume DAgger explanation] (9:26-12:43)
-|[http://rll.berkeley.edu/deeprlcourse/ Berkeley Levine course] [http://rll.berkeley.edu/deeprlcourse/f17docs/lecture_2_behavior_cloning.pdf IL slides] (DAgger defined on slide 14); [https://katefvision.github.io/katefSlides/immitation_learning_I_katef.pdf CMU Fragkiadaki IL slides] (1-29)<br>''Paper presentation choice due''
+|[http://rll.berkeley.edu/deeprlcourse/ Berkeley Levine course] [http://rll.berkeley.edu/deeprlcourse/f17docs/lecture_2_behavior_cloning.pdf IL slides] (DAgger defined on slide 14); [https://katefvision.github.io/katefSlides/immitation_learning_I_katef.pdf CMU Fragkiadaki IL slides] (1-29)
+<br>''Homework due''
+|-
+|style="background:rgb(102, 204, 255)"|15
+|Oct. 22<br>''Withdraw deadline''
+|Reinforcement learning
+|[http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html UCL Silver course]: [http://nameless.cis.udel.edu/class_data/849_s2018/intro_RL.pdf RL slides] (7-42), [https://www.youtube.com/watch?v=2pWv7GOvuf0 RL video] (15:40-21:20, 1:02:13-1:04:40 contains movies missing from PDF); [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture14.pdf Stanford deep-Q lecture slides] (23-62); [https://www.nature.com/articles/nature14236.pdf original deep-Q paper]
+|
+<!--<br>''Paper presentation choice due''-->
 |-
 |16
-|Oct. 29
+|Oct. 24
-|Reinforcement learning
+|style="background:rgb(255, 102, 0)"|NO CLASS<br>''Break before presentations start''
+|
 |
-|[http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html UCL Silver course]: [http://nameless.cis.udel.edu/class_data/849_s2018/intro_RL.pdf RL slides] (7-42), [https://www.youtube.com/watch?v=2pWv7GOvuf0 RL video] (15:40-21:20, 1:02:13-1:04:40 contains movies missing from PDF); [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture14.pdf Stanford deep-Q lecture slides] (23-62); [https://www.nature.com/articles/nature14236.pdf original deep-Q paper]
 |-
 |style="background:rgb(102, 204, 255)"|17
-|Oct. 31
+|Oct. 29
 |Student paper presentation
-|
+|Maxim Bazik, "PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud"
 |
 |-
 |18
-|Nov. 5
+|Oct. 31
 |Student paper presentation
-|
+|Woosik Lee, "Learning Wheel Odometry and IMU Errors for Localization"
-|''Project proposal due Sunday, April 15''
+|<!--''Project proposal due Sunday, April 15''-->
 |-
 |style="background:rgb(102, 204, 255)"|19
-|Nov. 7
+|Nov. 5
 |Student paper presentations
-|
+|Yi Liu, "Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation"
 |
 |-
 |20
-|Nov. 12
+|Nov. 7
 |Student paper presentations
-|
+|Chuchu Chen, "Deep Learning for 2D Scan Matching and Loop Closure"<br>
+Chirantan Ghosh, "Video Action Transformer Network"
 |
 |-
 |style="background:rgb(102, 204, 255)"|21
-|Nov. 14
+|Nov. 12
 |Student paper presentation
-|
+|Sha Liu, "Robots that can adapt like animals"<br>
+Seyedalireza Khoshsirat, "PCN: Point Completion Network"
 |
 |-
 |22
-|Nov. 19
+|Nov. 14
 |Student paper presentations
-|
+|Shengye Li, "Learning Common and Specific Features for RGB-D Semantic Segmentaion with Deconvolutional Networks"<br>
+Eric Wright, "Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight"
 |<!--[https://docs.google.com/presentation/d/19Wm-H2ttW_m5pan_lCXDzmk468gLoud6Xug_2_i0mWI/edit?usp=sharing&authkey=CKmYmJYI slides]-->
 |-
 |style="background:rgb(102, 204, 255)"|23
-|Nov. 21
+|Nov. 19
 |Student paper presentation
-|
+|Shivanand Sheshappanavar, "PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows"<br>Zhang Guo, "Believe It or Not, We Know What You Are Looking at!"
 |
 |-
 |24
-|Nov. 25
+|Nov. 21
-|Instructor paper presentation
+|Student paper presentations<!--Instructor paper presentation: RL for locomotion-->
-|Rasmussen, [http://www.ri.cmu.edu/publication_view.html?pub_id=5590 "Real-Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels"], N. Fairfield, G. Kantor, and D. Wettergreen, JFR 2007
+|Nikhil Gothankar, "Fast Online Object Tracking and Segmentation: A Unifying Approach"<br>
-|[https://docs.google.com/presentation/d/1CTGsRG7yvJVfZyBfaC1ZpBoUJ3ALbLaYnMeYZDM1wRA/edit#slide=id.i31 DepthX slides]
+Vineet Singh, "Spatial Transformer Networks"<!--Rasmussen, [http://www.ri.cmu.edu/publication_view.html?pub_id=5590 "Real-Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels"], N. Fairfield, G. Kantor, and D. Wettergreen, JFR 2007-->
+|<!--[https://www.youtube.com/watch?v=G4lT9CLyCNw DeepLoco highlights video] (6:23)--><!--[https://docs.google.com/presentation/d/1CTGsRG7yvJVfZyBfaC1ZpBoUJ3ALbLaYnMeYZDM1wRA/edit#slide=id.i31 DepthX slides]-->
 |-
-|style="background:rgb(102, 204, 255)"|25
+|style="background:rgb(102, 204, 255)"|
+|Nov. 26
+|style="background:rgb(255, 102, 0)"|NO CLASS<br>''Thanksgiving break''
+|
+|
+|-
+|
 |Nov. 28
-|Final project review
+|style="background:rgb(255, 102, 0)"|NO CLASS<br>''Thanksgiving break''
 |
 |
 |-
-|
+|style="background:rgb(102, 204, 255)"|25
 |Dec. 3
-|style="background:rgb(255, 102, 0)"|NO CLASS<br>''Work on projects...''
+|Student paper presentation
-|
+|Md Mottalib, "An Empirical Evaluation of Deep Learning on Highway Driving"
 |
 |-
-|style="background:rgb(102, 204, 255)"|26
+|26
-|Dec. 5
+|Dec. 5, 6
-|Final project presentations part I
+|Final project presentations
 |
 |
 |}