Email and discussion

Canvas: https://canvas.uw.edu/courses/1448792

Please access Zoom class lectures via Canvas.

Discussion board: https://us.edstem.org/courses/4944/discussion/

Use this board to discuss the content of the course. Feel free to discuss homeworks, projects, and any confusion over topics discussed in class. It is also acceptable to ask for clarifications about the statement of homework problems, but not about their solutions.

Robot: Duckiebot with Jetson Nano

We managed to have Duckiebot robot kits sent to each student. These will be used in the homeworks and projects.

Textbook

There is no required textbook for the class. Many of the lectures and homework assignments will have associated papers and chapters from:

Probabilistic Robotics, S. Thrun, W. Burgard, and D. Fox., MIT Press, Cambridge, MA, September 2005.

Homework

There will be three homeworks with programming components done in Python.

• Homework #1, due April 20th, 11:59 pm (pdf, code)
• Homework #2, due May 11th, 11:59 pm (pdf)
• Homework #3, due May 27th, 11:59 pm (pdf, code)

Project

Projects will be done in teams of two or three. We expect that most projects will leverage the Duckiebots. However, we are open to alternatives should you have access to adeuquate project environments via your research.

• Project 0.5, due April 19th, 11:59 pm (pdf)
• Project 1, due May 7th, 11:59 pm (github)
• Project 2, due June 10th, 11:59 pm (pdf (revised))
• Project 2 Presentation, June 7th, 10:30 am - 12:30 pm

Grading

You will submit your assignments (both writeups and code) to gradescope. You have all been enrolled into the class. Please check your email.

• Homeworks (40%)
• Project (60%)

Late policy

You are allowed to use 6 late days throughout the quarter. After this, assignments turned in late will incur a penalty of 20%, for each day. Please plan ahead and don't expect more.

Academic Honesty Policy

While we encourage students to discuss homeworks, each student must write up their own solution. It’s fine to use a source for generic algorithms (with attribution), but it is not allowed to copy solutions to the problems. Additionally, students may not post their code online. If we determine that a student posted their code online, they will get an automatic 50% reduction on the entire assignment (math + code) and if they copy code for the problems from another student or from online, they will get an automatic 0% for the entire assignment.

Anonymous Feedback

Please send your feedback via https://feedback.cs.washington.edu/

Lectures

Date	Topic	Slides	Reading (textbook/papers)	Homework/Project
Mar 30	Introduction	Intro	Chapter 1, 2 of Probabilistic Robotics	-
Apr 1	Bayesian state estimation, Filtering	Prob Intro	Chapter 1, 2 of Probabilistic Robotics	-
Apr 2	-	-	-	Homework 1 posted
Apr 6	Bayesian state estimation, Neural Networks	Gaussians	Chapter 6, 9 of Deep Learning	-
Apr 8	Neural Networks	-	Chapter 6, 9 of Deep Learning	-
Apr 9	-	-	-	Project 0.5 posted
Apr 13	Motion and Sensor Models	Motion models, sensor models	Chapter 5, 6 of Probabilistic Robotics	-
Apr 14	-	-	-	Lab Session 1-3 pm
Apr 15	Sensor Models, Kalman Filters (linear, EKF)	Kalman filters	Chapters 3 and 7 (skip 3.5 and derivations)	-
Apr 16	-	-	-	Project 1 posted
Apr 19	-	-	-	Project 0.5 Due
Apr 20	Kalman Filters (linear, EKF)	-	Chapters 3 and 7 (skip 3.5 and derivations)	Homework 1 Due Project 1 posted
Apr 22	Particle Filters	Particle filters	Chapters 4 and 8 (skip derivations)	-
Apr 27	Particle Filters II	Multi localization, KLD-Sampling	-	-
Apr 29	Occupancy maps, SLAM	Occupancy mapping, SLAM	Chapters 9 and 10, Octomaps	Homework 2 posted Project 2 Proposal Due
May 4	SLAM and Graph-SLAM		GTSAM, Chapter 11 and 13	-
May 6	Fast-SLAM	Fast-SLAM + Pose-RBPF	-	-
May 7	-	-	-	Project 1 Due
May 11	Pose-RBPF	-	Pose-RBPF paper	Homework 2 Due
May 13	Exploration	Exploration	Multi-robot, Curiosity-driven learning, Object modeling	Homework 3 posted Project 2 Mid-progress Report Due
May 18	Deterministic Planning	Det planning	Motion planning, A* slides from 473	-
May 20	Sampling-based Planning	Sampling-based planning	Complex motion planning, Anytime and replanning A*, RRT	-
May 25	Markov Decision Process	-	Chapter 14	-
May 27	Inverse Reinforcement Learning	-	-	Homework 3 Due
Jun 1	Learning to Grasp	-	GraspNet, clutter	-
Jun 3	Recap	-	SE3-Nets, Langrangian Networks	-
Jun 7	Project 2 Presentation, 10:30 am - 12:30 pm
Jun 10	Project 2 Final Report Due