Thoughtfully deployed robots that learn to interact in many real world environments.


Intelligent & novel devices to interact with the physical world.

Machine Learning

The science of using data to make decisions in the presence of uncertainty.


Making sure the stakeholders of automation are in the conversation.

My research experience

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 here.

  1. Novel Robotics: I want to be able to build useful robots from whatever pieces an engineer has.
  2. Model-based Reinforcement Learning: I am optimistic about interpretable learning for robots.
  3. 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.

Recent Papers

Paper pages have abstracts, videos, take-aways and more!

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks

We study three developing subfields of AI research and their growing relationship with the sociotechnical: AI Safety, Fair Machine Learning, and Human-in-the-loop Autonomy.

Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning

We explored how MBRL can learn multi-step, nonlinear controllers!

Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning

Trying to reframe the MBRL framework with long-term predictions instead of one-step predictions!

Learning for Microrobot Exploration: Model-based Locomotion, Robust Navigation, and Low-Power Deep Classification

A collections of steps towards a data-driven autonomous microrobot.

Objective Mismatch in Model-based Reinforcement Learning

Studying the numerical effects of a dual-optimization problem in model-based reinforcement learning -- control and dynamics. When optimizing model accuracy, there is no guarantee on improving task performance!

Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning

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

We used deep model-based reinforcement learning to have a quadrotor learn to hover from less than 5 minutes of all experimental training data.

Toward Controlled Flight of the Ionocraft: A Flying Microrobot Using Electrohydrodynamic Thrust With Onboard Sensing and No Moving Parts

A collection of steps towards controlled flight of The Ionocraft, a completely silent microrobot with ion thrust!