Instructor: Matt Travers; TA: Jaskaran Grover

This class will introduce concepts from a broad spectrum of constrained optimization, optimal and feasible motion planning, as well as machine learning all in the context of controlling complex, contemporary robotic systems.  The specific systems on which we will focus include nominal examples from the nonlinear control literature (e.g., the constrained simple pendulum, the acrobat, cart-pole, etc.) all the way to the hybrid systems that underly much of the leading work on humanoid robots (e.g., the compass gait walker, the SLIP model, etc.).  The course material will primarily consist of lecture notes as well as seminal papers from the robotics literature.  By the end of this class you will be able to readily understand, implement, and adapt methods that span a wide spectrum that includes optimal control theory and reinforcement learning.
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matthew travers,
Aug 30, 2018, 8:28 PM
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matthew travers,
Sep 4, 2018, 10:57 AM
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