Introduction to Robotics

Instructor: Stefanos Nikolaidis (nikolaid at usc dot edu)

TAs: Chris Denniston, Heramb Nemlekar, Hejia Zhang

Content Contributors: Hejia Zhang, David Millard, Aravind Kuramaguru, Gautam Salhotra

Lectures: Mon / Wed 3:30 - 4:50pm (online only)

Office Hours: Mon 5:30 - 6:30pm (online only)

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 a solid background 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.


Component Percentage
Lab Assignments 40%
HW Assignments 30%
Final Exam 30%

Assessment of Assignments

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

Tentative Schedule:

Date Lecture Topic Assignment (Released)
Readings Notes
Aug 24 1 Introduction Lecture 1, Slides
Aug 26
2 Matrix Algebra Refresher SS Appendix A Slides
Aug 31
3 Probability Theory HW (Math Fundamentals) RN Ch. 13-14 Lecture 3, Slides
Sep 2
4 Python / ROS Tutorial Lab 1
Sep 7
Labor day (no class)
Sep 9
4 Bayesian Networks Slides
Sep 14
5 Linear Dynamical Systems Lecture 5, Slides
Sep 16
6 Bayesian Filters TBF Ch. 2, 3.1-3.2.3 Lecture 6, Slides
Sep 21
7 Kalman Filters and EKF HW2 (KF/EKF) TBF Ch. 3.3.1-3.3.3, 4.3.1-4.3.2 Lecture 7, Slides
Sep 23
8 Particle Filters, Motion Models TBF Ch. 5.3.2, 5.4.2, 6.3.1, 6.6.2 Lecture 8, Slides
Sep 28
9 Sensor Models, Localization TBF Ch. 7.4.1, 7.4.2, 7.5.1, 9.2 - excluding 9.2.1 Lecture 9, Slides
Sep 30
10 Mapping and SLAM Lecture 10, Slides
Oct 5
11 AIKIDO Tutorial AIKIDO (Personal Robotics Lab, University of Washington)
Oct 7
12 Midterm review Lab 2 (Localization) Slides
Oct 13
13 Mathematical Programming Lecture 13, Slides
Oct 14
14 Configuration Spaces CL 3.1,3.2, 3.5.1 Lecture 14, Slides
Oct 19
15 Kinematic Transformations HW3 (FK/IK) CL 3.6-3.8 Lecture 15, Slides
Oct 21
16 Inverse Kinematics Lecture 16, Slides
Oct 26
17 Sampling-based
Motion Planning I
CL 7.1.1, 7.2.2 Slides
Oct 28
18 Sampling-based
Motion Planning II
HW4 (RRT) CL 7.3.3, LA 7.3.1 Lecture 18, Slides
Nov 2
19 Learning from Physical Interactions Lab 3 (Motion Planning) Learning with Human Adversaries
Nov 4
20 Special Topics in Robotics
Nov 9
21 Task Space Regions Lab 4 (Task Space Regions) Task Space Regions Lecture 21, Slides
Nov 11
22 Dynamics SS Ch. 7.1.1 Lecture 22, Slides
Nov 16
23 Non-Linear Control SS Ch. 8.6.2, Appendix C3 Lecture 23, Slides
Nov 18
24 Final Review Slides

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