CATALOG DESCRIPTION: A laboratory-based introduction to robotics. Focus will be on both hardware (sensors and actuators) and software (sensor processing and behavior development). Topics will include: the basics in kinematics, dynamics, control, and motion planning; and an introduction to Artificial Intelligence (AI) and Machine Learning (ML). Formerly EECS 295.

  • This course fulfills the AI Depth requirement.

PREREQUISITES: Some programming experience (110 or 111), or permission of instructor.

REQUIRED TEXT: The Robotics Primer, MIT Press 2007, Maja J. Matarić.

COURSE COORDINATOR: Prof. Brenna Argall

DETAILED COURSE TOPICS: Open-loop control, with different types of hardware/motion (mobility, manipulation); Closed-loop control and sensor processing, with different types of sensors; Reactive control, behavior-based robotics, reasoning about uncertainty; Basics in AI and ML, with simple learning techniques.

ASSIGNMENTS: Coursework will consist primarily of laboratory assignments, that include (some) hardware construction and (more) software development. Laboratory work will be done in groups of 2-3 students. Each lab assignment will be demoed and written up as a report (individually).

COURSE OBJECTIVES: At the end of this course students should understand the fundamentals of autonomous robot operation, and be able to program a robot to read from its sensors and perform simple (hard-coded and learned) tasks.