Introduction to Robotics

Instructor: Stefanos Nikolaidis (nikolaid at usc dot edu)

TAs: Ya-Chuan Hsu, Heramb Nemlekar, Gautam Salhotra, Bryon Tjanaka

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

Lectures: Mon / Wed 10:00 - 11:50am LVL17

Office Hours: Mon 12pm - 1pm

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 23
1 Introduction Slides
Aug 25
2 Matrix Algebra Refresher SS Appendix A Slides
Aug 30
3 Probability Theory HW (Math Fundamentals) RN Ch. 13-14 Slides
Sep 1
4 Python / ROS Tutorial Lab 1
Sep 6
Labor day (no class)
Sep 8
5 Bayesian Networks Slides
Sep 13
6 Bayesian Filters TBF Ch. 2, 3.1-3.2.3 Slides
Sep 15
7 Linear Dynamical Systems Slides
Sep 20
8 Kalman Filters and EKF HW2 (KF/EKF) TBF Ch. 3.3.1-3.3.3, 4.3.1-4.3.2 Slides
Sep 22
9 Particle Filters, Motion Models TBF Ch. 5.3.2, 5.4.2, 6.3.1, 6.6.2 Slides
Sep 27
10 Sensor Models, Localization TBF Ch. 7.4.1, 7.4.2, 7.5.1, 9.2 - excluding 9.2.1 Slides
Sep 29
11 Mapping and SLAM Slides
Oct 4
12 AIKIDO Tutorial AIKIDO (Personal Robotics Lab, University of Washington)
Oct 6
13 Midterm review Lab 2 (Localization) Slides
Oct 11
14 Mathematical Programming Slides
Oct 13
15 Configuration Spaces CL 3.1,3.2, 3.5.1 Slides
Oct 18
16 Kinematic Transformations HW3 (FK/IK) CL 3.6-3.8 Slides
Oct 20
17 Inverse Kinematics Slides
Oct 25
18 Sampling-based
Motion Planning I
CL 7.1.1, 7.2.2 Slides
Oct 27
19 Sampling-based
Motion Planning II
HW4 (RRT) CL 7.3.3, LA 7.3.1 Slides
Nov 1
20 Special Topics in Robotics Lab 3 (Motion Planning)
Nov 3
21 Special Topics in Robotics
Nov 8
22 Task Space Regions Lab 4 (Task Space Regions) Task Space Regions Slides
Nov 10
23 Dynamics SS Ch. 7.1.1 Slides
Nov 15
24 Non-Linear Control SS Ch. 8.6.2, Appendix C3 Slides
Nov 17
25 Markov Decision Processes
Nov 22
26 Quality Diversity Optimization
Nov 24
Thanksgiving Holiday (no class)
Nov 29
27 TBD
Dec 1st
28 Final Review Slides

Additional Policies: Please read the statement on academic conduct and student support systems.