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G22-3033-003, Fall 2007:
Mobile Robots

[ Course Homepage | Schedule and Course Material | Mailing List ]

Seminar/Workshop Course on Mobile Robotics

Instructor: Yann LeCun, 715 Broadway, Room 1220, x83283, yann [ a t ] cs.nyu.edu

Classes: Wednesdays 1:25-3:15PM, Room 1221, 715/719 Broadway.

Office Hours for Prof. LeCun: Wednesdays 5:00-7:00 PM

Click here for schedule and course material >>>

Course Description

This course will cover methods and algorithms used in modern mobile robotics systems, including sensors, 3D vision, occupancy maps, visual odometry, Kalman filtering, simultaneous location and mapping, path planning, on-line learning and related topics.

This course will combine three types of sessions:

Who Can Take This Course?

Prerequisite: student should have some experience either in computer vision/image processing, or in machine learning and pattern recognition. Good programming abilities and solid math background are required.

The course will include a significant amount of code implementation on actual robots.

Due to the nature of the course, the maximum number of students is limited.

Future editions of the course will be open to a wider audience.

Topics Treated

The topics studied in the course include:
  • sensors and sensor processing
  • vision and 3D reconstruction
  • Kalman filtering
  • Pose prediction
  • Map building
  • Path planning
  • Visual odometry
  • Simultaneous Location and Mapping
  • Autonomous Learning
  • Reinforcement Learning
Projects will be performed on Roomba robots equiped with Mac Minis and Firewire stereo cameras. Final projects may be run on the LAGR robot.

The LAGR project


Evaluation will be based on class participation and project implementation.

Automatic Face Detection

Mailing List

Register to the course's mailing list.

Text Books

Probabilistic Robotics Sebastian Thrun, Wolfram Burgard and Dieter Fox.

Introduction to Autonomous Mobile Robots Roland Siegwart and Illah R. Nourbakhsh

automatic obstacle avoidance

Robotics Research at NYU

Please have a look at the research project page of the Computational and Biological Learning Lab for a few example of machine learning research at NYU.

There are numerous opportunities for independent studies and even undergraduate research projects. Contact Prof. LeCun for details.



  • Lush: A simple language for quick implementation of, and experimentation with, numerical algorithms (for Linux, Mac, and Windows/Cygwin). Many algorithms described in this course are implemented in the Lush library. Lush is available on the department's Sun machines that are freely accessible to NYU graduate students. See Chris Poultney's notes on installing Lush under Cygwin.
  • Torch: A C++ library for machine learning.

Lush is installed on the department's PCs. It will soon be available on the Sun network as well.