Explore planning and control concepts in this comprehensive lecture. Dive into state transition equations, numerical examples of various actions, and PyTorch implementations of physical scenarios. Learn about the Kelley-Bryson algorithm, control with final and cumulative costs, and optimal control examples. Gain insights from practical demonstrations and quizzes throughout the session, enhancing your understanding of deep learning applications in planning and control systems.
Overview
Syllabus
– Background on the class creation
– Take a quiz!
– Planning and control
– Action plan table of contents
– State transition equations
– A few numerical examples I, no action
– A few numerical examples II, negative acceleration
– A few numerical examples II, positive and negative steering
– PyTorch implementation of physical examples
– Kelley-Bryson algorithm RNN recap and control
– Control with final cost
– Control with cumulative cost
– PyTorch implementation of optimal control examples
Taught by
Alfredo Canziani
