Instructors

Siddhartha Srinivasa and Arunkumar Byravan

TAs

Kendall Lowrey and Patrick Lancaster

Lectures: MWF 10:30-11:20, MOR 234
Quiz Sections: Th 9:30-10:20 EEB 045 / Th 10:30-11:20 MGH 241
Office Hours: Mon 3:30-5:30, Tues 1:30-3:30, Wed 2:30-4:30, SIG 322
Discussion Board and Assignments: Canvas

Overview

We will cover topics related to state estimation (particle filters, motion models, sensor models etc), planning/control (search based planners, lattice based planners, trajectory following techniques etc), and perception and learning (object detection, learning from demonstrations etc.). Each of the 3-4 assignments will involve student teams implementing the algorithms learned in lecture on 1/10th sized rally cars. Concepts from all of the assignments will culminate into a partially open-ended final project with a final demo on the rally cars. The course will involve programming in a Linux and Python environment along with ROS for interfacing to the robot.
Prerequisites: CSE 332 (required), MATH 308 (recommended), CSE 312 (recommended)
Programming Tools: Python, Numpy, ROS (Python), PyTorch (Optional)
Grading (Tentative): 60% Assignments, 5% Class Participation, 35% Final Project
Textbooks: Probabilistic Robotics (Optional) - S. Thrun, W. Burgard, and D. Fox., MIT Press, Cambridge, MA, September 2005.

Schedule

Date Topic Slides Recommended Reading Homework
Jan 3 Introduction Lecture 1 Python, Numpy Assignment 0
Jan 4 ROS and Racecar Tutorial RACECAR Components, ROS Basics, HW0 Overview ROS (ROS Supplement) -
Jan 5 Anatomy of an Autonomous Vehicle Lecture 2 ROS (ROS Supplement) -
Jan 8 Intro to State Estimation Lecture 3 Prob Robotics CH 2 -
Jan 10 Bayes Filters - MacKay CH 2 -
Jan 12 Motion Models Lecture 5 Prob Robotics CH 5 -
Jan 15 - - Assignment 1
Jan 17 Motion Model Noise and Sensor Models - Prob Robotics CH 5 & 6 -
Jan 19 Particle Filters Derivation - Prob Robotics CH 4 -
Jan 22 Particle Filters Cont'd - Prob Robotics CH 4 & 8, Notes -
Jan 24 Particle Filter Difficulties, Tricks of the trade - Notes -
Jan 25 HW 1 Discussion HW 1 Example Figures - -
Jan 26 Occupancy Grid Mapping - Prob Robotics CH 9 -
Jan 29 Manipulation, Manifold Particle Filter - - -
Jan 31 SLAM Lecture 12 Prob Robotics CH 10 & 13 -
Feb 2 Local control - PID Lecture 13 Additional PID Resource Assignment 2
Feb 5 Model Predictive Control - - -
Feb 7 Image Processing & Projective Geometry Lecture 15 - -
Feb 9 Model Learning Lecture 16 - -
Feb 12 Supervised Learning Lecture 17 - -
Feb 14 Supervised Learning Cont'd Lecture 18 - -
Feb 16 Neural Networks Lecture 19 Pytorch Tutorial Assignment 3
Feb 21 Model Predictive Path Integral Control Lecture 20 - -
Feb 23 Linear Quadratic Regulation Lecture 21 - -
Feb 26 Introduction to Planning Lecture 22 - Final Project Spec, Dubins Planning
Feb 28 Planning on Roadmaps Lecture 23 - -
Mar 02 Lazy Search Lecture 24 - -
Mar 05 Introduction to Deep Reinforcement Learning Lecture 25 - -
Mar 07 SDFs Tracking and Mapping Lecture 26 - -
Mar 09 Quadcopter Control - -


Robot Platform