Time and Place:
Thursdays 16:00-18:30 in HNB 107
- Discuss statistical learning methods for actual robot applications
- Projects possible with simulated or actual robots
In a seminar style, this course will discuss the most recent
developments in statistical learning for learning with complex robots.
The style of the course will focus on studying selected papers from
the literature and to work on projects. Potential projects include
applications on complex robots like humanoid-robots, arm-robots,
head-robots, or a dog-robot in the lab of the instructor, or other
real/simulated system. Topics include real-time learning methods for
regression, reinforcement learning, probabilistic methods of sensor
processing like Kalman filtering and particle filtering, imitation
learning, dimensionality reduction techniques, Bayesian techniques for
statistical learning, etc.
This is a seminar style class, with reading assignment, paper presentations, and projects.
- 25% active participation in class
- 50% paper presentations
- 25% project
Some possible projects are:
LittleDog Data Files:
LittleDog Simulator and General Manual:
CS545, CS542 or CS567, or any other graduate level classes that provided the foundation of machine learning and robotics, or permission by instructor.
Dr. Stefan Schaal
University of Southern California
Ronald Tutor Hall RTH-401
Los Angeles, CA 90089-2520
phone: (213) 740 9418
email: (:email firstname.lastname@example.org :)
According to email arrangement with instructor (:email email@example.com :).
All students are required to abide by the USC code of Academic Integrity. Violation of that Code will be dealt with as described in SCAMPUS. If you have any questions about the responsibilities of either students, faculty, or graders under this policy, contact the instructor or the Office of Student Conduct.
Disabilities and Academic Accommodations:
Students requesting academic accommodations based on a disability are required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP when adequate documentation is filed. Please be sure the letter is delivered to the instructor (or TA) as early in the semester as possible. DSP is open Monday-Friday, 8:30-5:00. The office is in Student Union 301 and their phone number is (213) 740-0776.