Title: Machine Learning for Safe and Effective Robotic Control

Date: Monday, July 26, 2021

Time: 10AM - 12PM EST

Location (Virtual): https://bluejeans.com/707943714/4137

Nolan Wagener

Robotics PhD Student

School of Interactive Computing

Georgia Institute of Technology

Committee:

Dr. Byron Boots (Advisor) - School of Computer Science and Engineering, University of Washington

Dr. Panagiotis Tsiotras (Co-Advisor) - School of Aerospace Engineering, Georgia Institute of Technology

Dr. Sehoon Ha - School of Interactive Computing, Georgia Institute of Technology

Dr. Seth Hutchinson - School of Interactive Computing, Georgia Institute of Technology

Dr. Andreas Krause - Department of Computer Science, ETH Zurich

Abstract:

For robotic systems to take greater roles in industrial and public settings, there is a great need for them to operate in unstructured or dynamic environments. With this increase in task and environment complexity, it is necessary for robotic systems to learn from interactions and data coming from the environment.

This research studies several ways that learning approaches can be incorporated for control tasks: system identification, model predictive control (MPC), and safe reinforcement learning. More specifically, a neural network approach to modeling rally car dynamics will be presented which, along with a sampling-based MPC algorithm, results in state-of-the-art aggressive off-road driving. Then, the MPC framework will be re-examined from an online learning perspective. Finally, a safe reinforcement learning algorithm based on interventions will be presented, and proposed work will focus on an application of the algorithm to a quadrupedal robot for locomotion.