Optimal and Learning Control for Robotics

Catalog description

What kind of movements should a robot perform in order to walk, jump or manipulate objects? Can it compute optimal behaviors online? Can it learn this directly from trial and error? This course will introduce modern methods for robotics movement generation based on numerical optimal control and reinforcement learning. It will cover fundamental topics in numerical optimal control (Bellman equations, differential dynamic programming, model predictive control) and reinforcement learning (actor-critic algorithms, model-based reinforcement learning, deep reinforcement learning) applied to robotics. It will also contain hands-on exercises for real robotic applications such as walking and jumping, object manipulation or acrobatic drones. Recommended background in at least one of the following: linear systems; robotics; machine learning; convex optimization; programming (python or C++). | Prerequisites: ECE-GY 6253 or ME-GY 6703 or ME-GY 6923 .