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:

• demonstrate proficiency in the theoretical tools that support state estimation, manipulation and planning with sensor and modeling noise.
• implement these techniques and test them with real-world datasets.
• integrate your algorithms with state-of-the-art simulation environments
• critique a research paper’s methods and analysis
• explain the computational and practical challenges of applying these techniques in real-world interaction settings and compare them in terms of robustness, scalability and performance.

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:

• Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Pearson Education Limited, 2016. (RN)
• Sebastian Thrun,, Wolfram Burgard, and Dieter Fox. Probabilistic robotics. MIT press, 2005. (TBF)
• Howie Choset, Kevin M. Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia E. Kavraki, and Sebastian Thrun. Principles of robot motion. (CL)
• Steven Lavalle. Planning algorithms. (LA)
• Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, and Giuseppe Oriolo. Robotics: Modelling, Planning and Control (SS).

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

Grading:

Assessment of Assignments

• Lab Assignments: There will be 3-4 lab assignments, which will involve applying the techniques taught in the classroom to robotic systems using real-world data. We will ask you to submit a report for each lab assignment showcasing your results. Lab assignments 3 and 4 use the AIKIDO software platform from the Personal Robotics Lab at the University of Washington.
• Homework Assignments: The homework assignments will have both theoretical and coding components that will exercise the techniques we will cover in the class and will help you prepare for the lab assignments.
• Final Exam: There will be a final exam on the material covered in the lectures and assignments. You can bring to the exam an A4 cheat sheet. No other material, or any electronic devices will be allowed.
• Participation: You will get the most out of this class if you are active and engaged. This includes asking questions and participating in discussions
• Scribing: You will take turns compiling the lecture notes. The scribing will expand on the lecture notes and reading, and students will be expected to include figures and reference. Scribes should delived the notes within five days of the lecture for review.

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:

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.