Tyler Han

I am currently a PhD student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. I am part of the Robot Learning Lab where I am advised by Byron Boots. I am also an NSF Graduate Research Fellow.

Prior to UW, I completed my B.S. in Aerospace Engineering and B.S. in Computer Science at the University of Maryland, College Park. During my undergrad, I worked with Glen Henshaw and Patrick Wensing while at the Naval Research Laboratory in Washington, D.C.

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Research

I am generally interested in techniques that combine machine learning and engineering. Within robotics, I am particularly interested in ill-defined engineering and autonomy challenges. Often, these problems require some "human intuition" to help constrain the solution. Some techniques I have been investigating involve learning from demonstration (LfD) and Inverse Reinforcement Learning (IRL).

Model Predictive for Aggressive Driving over Uneven Terrain

Terrain traversability in off-road autonomy has traditionally relied on semantic classification or resource-intensive dynamics models to capture vehicle-terrain interactions. However, our experiences in the development of a high-speed off-road platform have revealed several critical challenges that are not adequately addressed by current methods at our operating speeds of 7—10 m/s. [...]

Miscellaneous Projects

Other projects that are not necessarily research related or not published

Off-Road Self-Driving

Since beginning my PhD, I have been primarily focused on work related to the DARPA RACER program as part of the UW team. The robot itself is a modified Polaris RZR with onboard sensing and compute. My work on the team has consisted of a range of jobs: [...]

Learning Motor Primitives

In an undergraduate project, I tackled part of the challenge of teaching robots to perform motor skills from a small number of demonstrations. We proposed a novel approach by joining the theories of Koopman Operators and Dynamic Movement Primitives to Learning from Demonstration. Our approach, named Autoencoder Dynamic Mode Decomposition [...]

Forked from Leonid Keselman's Website