I'm interested in the intersection of machine learning and control, with applications to experimental robotics.
With Kris, I am working on direct synthesis of robot controllers with model-based reinforcement learning where we do not need any past system knowledge.
For an overview of my recent work, you can find a shortened version of my qualifying exam slides
here, or a private recording
My high level interests.
- Novel Robotics: I want to be able to build useful robots from whatever pieces an engineer has.
- Model-based Reinforcement Learning: I am optimistic about interpretable learning for Locomotion of
- Robot Learning in Weak-sensor Environments: As a practical roboticist (or a data-scientist), I want to
make systems that work in all parts of the world.
Objective Mismatch in Model-based Reinforcement Learning
Learning for Decision and Control, 2020.
Studying the numerical effects of a dual-optimization problem in model-based reinforcement learning.
When optimizing model accuracy, there is no guarantee on improving task performance!
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
IEEE Robotics and Automation Letters (RA-L), 2019.
We used deep model-based reinforcement learning to have a quadrotor learn to hover from
less than 7 minutes of all experimental training data. No system knowledge was needed
for these experiment, reading raw sensor values and commanding motor PWMs.
Toward Controlled Flight of the Ionocraft: A Flying Microrobot Using
Electrohydrodynamic Thrust With Onboard Sensing and No Moving Parts
Craig Schindler, Kris Pister
IEEE Robotics and Automation Letters (RA-L), 2018.
A collection of steps towards controlled flight of The Ionocraft, a completely silent
microrobot with ion thrust!
Last updated 10 June 2020,
this guy makes a nice website.