Nathan Lambert's Work
I'm interested in the intersection of machine learning and control, with applications to
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
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 robots.
- 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.
Learning for Microrobot Exploration: Model-based Locomotion, Robust Navigation,
and Low-Power Deep
Kristofer S.J. Pister
International Conference on Manipulation, Automation and Robotics at Small Scales,
A collections of steps towards an autonomous microrobot.
Recent work has pushed capabilities of the device forward, but little progress has been
made in creating an autonomous platform.
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!
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement
International Conference on Robotics and Automation, 2020.
Learning how to walk with a real-world hexapod using a hierarchy of model-free RL for
basic motion primitives
with model-based RL for higher level planning.
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!
Enhanced Lithium Niobate Pyroelectric Ionizer for Chip-Scale Ion
Mobility-Based Gas Sensing
K.B. Vinayakumar, Ved Gund,
Nathan Lambert, S Lodha, Amit Lal
IEEE Sensors, 2016.
We used a pyroelectric crystal to cause dielectric breakdown events in the air, which
can be used for chip scale ion based gas sensing.