A simulator for studying high-agent-count networked systems!

We present a concise set of directions for understanding the societal risks of new directions of AI research.

An open-source PyTorch repository designed from the bottom up for model-based reinforcement learning research.

We showed that advancements in AutoML when paired with common deep RL tasks, MBRL algorithms perform so well they break the simulator.

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.

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

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

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

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 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.

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

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