Introduction to Robotics

Instructor: Stefanos Nikolaidis (nikolaid at usc dot edu)

TAs: Aravind Kuramaguru, David Millard, Gautam Salhotra

Content Contributors: Hejia Zhang, Ahmed Fayed

Lectures: Mon / Wed 3:30 - 4:50pm, LVL 17

Office Hours: Mon / Wed 5:30 - 6:30pm, RTH 401

Course Description: This class will introduce students to the fundamental questions in robotics: what are good models of the world and how to integrate them reliably into the planning of deployed robotic systems physically interacting with the environment. All these problems arise from the uncertainty due to sensor noise, modeling limitations, approximations in algorithmic computations and inherent unpredictability of action outcomes. The course will explore probabilistic techniques that allow robots to act reliably and exhibit a variety of different behaviors in spite of different sources of uncertainty. We will first cover algorithms for state estimation in both known and unknown environments. We will then explore functional aspects of robot’s interaction with the world, such as the geometry of configuration spaces and manipulation planning in these spaces. We will wrap up the course by exploring the interplay of inference and planning and its applications in robot autonomy and human-robot interaction.

Learning Objectives: In this course, you will be introduced to probabilistic techniques that allow state estimation, manipulation and planning in robotics. By the end of this course you should be able to:

Prerequisites: Students are required to have knowledge of probability theory, linear algebra and calculus. Programming knowledge of Python is also required.

Reading Material: There is no required textbook for this course. The lecture material is available online. Much of the lecture material is taken from these books:

The assignments and final exam will be based only on material covered in the lectures.

Grading:

Component Percentage
Lab Assignments 35%
HW Assignments 25%
Final Exam 20%
Participation 10%
Scribing 10%

Assessment of Assignments

Note: Regardless of the grading system, you are required to submit all homework assignments, lab assignments, take the final exam and regularly attend lectures to receive a passing grade for the class.

Tentative Schedule:

Date Lecture Topic Assignment (Released)
Readings Notes
Aug 26 1 Introduction Lecture 1
Wed
Aug 28
2 Matrix Algebra Refresher SS Appendix A Scribing:template.tex
Mon
Sep 02
Labor Day (no class)
Wed
Sep 04
3 Probability Theory, Bayesian Networks HW (Math Fundamentals) RN Ch. 13-14 Lecture 3
Mon
Sep 09
4 Python / ROS Tutorial Lab 1
Wed
Sep 11
5 Linear Dynamical Systems Lecture 5
Mon
Sep 16
6 Bayesian Filters TBF Ch. 2, 3.1-3.2.3 Lecture 6
Wed
Sep 18
7 Kalman Filters and EKF HW2 (KF/EKF) TBF Ch. 3.3.1-3.3.3, 4.3.1-4.3.2 Lecture 7
Mon
Sep 23
8 Particle Filters, Motion Models TBF Ch. 5.3.2, 5.4.2, 6.3.1, 6.6.2 Lecture 8
Wed
Sep 25
9 Sensor Models, Localization TBF Ch. 7.4.1, 7.4.2, 7.5.1, 9.2 - excluding 9.2.1 Lecture 9
Mon
Sep 30
10 Mapping and SLAM Lecture 10
Wed
Oct 02
11 Mathematical Programming Lab 2 (Localization) Lecture 11
Mon
Oct 07
12 AIKIDO Tutorial AIKIDO (Personal Robotics Lab, University of Washington)
Wed
Oct 09
13 Introduction to Haptics (Guest Lecture: Heather Culbertson) Slides
Mon
Oct 14
14 Configuration Spaces CL 3.1,3.2, 3.5.1 Lecture 14
Wed
Oct 16
15 Kinematic Transformations HW3 (FK/IK) CL 3.6-3.8 Lecture 15
Mon
Oct 21
16 Inverse Kinematics Lecture 16
Wed
Oct 23
17 Sampling-based
Motion Planning I
CL 7.1.1, 7.2.2 Slides
Mon
Oct 28
18 Sampling-based
Motion Planning II
HW4 (RRT) CL 7.3.3, LA 7.3.1 Lecture 18
Wed
Oct 30
19 Learning from Physical Interactions Learning with Human Adversaries
Mon
Nov 04
20 Special Topics in Robotics Lab 3 (Motion Planning)
Wed
Nov 06
21 Special Topics in Robotics
Mon
Nov 11
22 Dynamics SS Ch. 7.1.1 Lecture 22
Wed
Nov 13
23 Non-Linear Control SS Ch. 8.6.2, Appendix C3 Lecture 23
Mon
Nov 18
24 Task Space Regions Lab 4 (Task Space Regions) Task Space Regions Lecture 24
Wed
Nov 20
25 Markov Decision Processes RN Ch. 17.1, 17.2 (Making Complex Decisions) Lecture 25
Mon
Nov 25
26 Lab Discussion / Office Hours
Wed
Nov 27
Thanksgiving (no class)
Mon
Dec 02
27 Recap I
Wed
Dec 04
28 Recap II

Additional Policies: Please read the statement on academic conduct and student support systems. Unless you are assigned to compile lecture notes, please refrain from using laptops or other electronic devices during class.