CSE 571: AI-Based Mobile Robotics

Spring 2021
TTh 10:00-11:20, online

Instructor

Dieter Fox (Office hours: Fri 10:30-11:30 am)

Teaching Assitants

Adam Fishman (Office hours: Tue/Thu 3:00-4:00 pm)
Junha Roh
Karthik Desingh (Robot support)

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 25th, 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))

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 Fast-SLAM GTSAM, Chapter 11 and 13 -
May 6 Fast-SLAM - - -
May 7 - - -

Project 1 Due

May 11 Pose-RBPF - -

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 - Motion planning, A* slides from 473

Homework 3 posted

Project 2 Mid-progress Report Due

May 20 Sampling-based Planning - Complex motion planning, Anytime and replanning A*, RRT -
May 25 Markov Decision Process - Chapter 14 -
May 27 Inverse Reinforcement Learning - - -
Jun 1 Learning to Grasp - GraspNet, clutter -
Jun 3 Recap - SE3-Nets, Langrangian Networks -
Jun 8

Project 2 Presentation

Jun 10

Project 2 Final Report Due