CS 4610/5335: Robotics Science and Systems (Spring 2019)

Brief Course Description

This course will introduce you to many of the foundational planning, control, and perception algorithms in robotics through the lens of robotic manipulation. You will implement each of these algorithms in a simplified context in Matlab. Then, you'll implement something more realistic in OpenRAVE, a simulation and planning toolchain used in the robotics manipulation research community. The focus will be on understanding and applying the ideas in new ways. There will be an end-of-semester project where you can show off what you've learned.

We will study most of the following topics (subject to change; see schedule tab above):

1. Kinematics/zero-order control
• Representation of rotation
• Manipulator forward kinematics
• Manipulator differential kinematics
• Cartesian end-effector control
2. Planning, Control
• Sample-based planning methods (RRT, PRM)
• Trajectory optimization methods
• Markov Decision Processes
• Linear optimal control (LQR, TVLQR)
3. Filtering, Localization, and Mapping
• Kalman filter, EKF
• SLAM algorithms
4. Computer Vision, Perception in Point Clouds
• Point cloud methods: RANSAC, ICP, etc.
• Object Detection Using Deep Learning
• Other applications of Deep Learning

Textbooks

The main textbook used in this course is Robotics, Vision, and Control: Fundamental Algorithms in Matlab by Peter Corke (first edition). You should buy this book. Additional material is taken from on-line sources that will be linked from the schedule page.

Prerequisites

There are two informal pre-requisites for this course: 1) a working knowledge of linear algebra and 2) the ability to program in Python and Matlab. If you need a linear algebra refresher, have a look at the Khan Academy lecture links below:

Vector intro for linear algebra
Introduction to the matrix
Multiplying matrices
Introduction to the identity matrix
How to transform vectors using a transformation matrix
Introduction to eigenvalues and eigenvectors

Academic Integrity

Cheating and other acts of academic dishonesty will be referred to OSCCR (office of student conduct and conflict resolution). See this link.

Lateness Policy

Late assignments will be penalized by 10% for each day late. For example, if you turned in a perfect homework assignment two days late, you would receive an 80% instead of 100%.

Instruction Staff

Primary Instructor:
Robert Platt ( r [dot] platt [at] neu [dot] edu )
Office hours: Tuesdays and Fridays, 2:15-3:15pm, 526 ISEC, or by Appt.

TAs:
Sandip Samal, samal.s@husky.neu.edu
Office hours: Mondays 2--3:30pm, 1st flr WVH
Sreejith Unnikrishnan, unnikrishnan.s@husky.neu.edu
Office hours: Thursdays 2--3:30pm, 1st flr WVH