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.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

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News

05/2024 "Model Predictive Control for Aggressive Driving Over Uneven Terrain" is accepted to R:SS 2024.
05/2024 "Dynamics Models in the Aggressive Off-Road Driving Regime" is accepted to ICRA 2024 Workshop on Resilient Off-Road Autonomy.
04/2024 Invited to Review for ICRA 2024 Workshop on Resilient Off-Road Autonomy
03/2023 Awarded the National Science Founation Graduate Research Fellowship (NSF GRFP)

Research

I am interested in using machine learning for automating systems with complex, real-world dynamics. Humans are inexplicably efficient with limited information and experience — not only in controlling their own bodies but also machines and tools. How can human efficiency and adaptability be formalized then transferred to data pipelines for robotics?

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Model Predictive Control for Aggressive Driving over Uneven Terrain


Tyler Han, Alex Liu, Anqi Li, Alex Spitzer, Guanya Shi, Byron Boots
Robotics: Science & Systems, 2024
website / arXiv

Terrain traversability in unstructured off-road autonomy has traditionally relied on semantic classification, resource-intensive dynamics models, or purely geometry-based methods to predict vehicle-terrain interactions. While inconsequential at low speeds, uneven terrain subjects our full-scale system to safety-critical challenges at operating speeds of 7–10 m/s. This study focuses particularly on uneven terrain [...]

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Dynamics Models in the Aggressive Off-Road Driving Regime


Tyler Han, Sidharth Talia, Rohan Panicker, Preet Shah, Neel Jawale, Byron Boots
Workshop on Resilient Off-Road Autonomy, ICRA, 2024
arXiv

Current developments in autonomous off-road driving are steadily increasing performance through higher speeds and more challenging, unstructured environments. However, this operating regime subjects the vehicle to larger inertial effects, where consideration of higher-order states is necessary to avoid failures such as rollovers or excessive impact forces. Aggressive driving through Model [...]

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Transferable Reinforcement Learning via Generalized Occupancy Models


Chuning Zhu, Xinqi Wang, Tyler Han, Simon Du, Abhishek Gupta
arXiv preprint, 2024
website / arXiv

Intelligent agents must be generalists, capable of quickly adapting to various tasks. In reinforcement learning (RL), model-based RL learns a dynamics model of the world, in principle enabling transfer to arbitrary reward functions through planning. However, autoregressive model rollouts suffer from compounding error, making model-based RL ineffective for long-horizon problems. [...]

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Learning Motor Primitives


Tyler Han, Carl Glen Henshaw
arXiv preprint, 2021
arXiv

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