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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News

Research

Animals need only to observe a behavior a handful of times before imitating them through experience. However, current machine learning methods require orders of magnitude more data to imitate a demonstation. I am interested in methods which enable robots to attain the same level of efficiency and robustness as animals.

Model Predictive Adversarial Imitation Learning for Planning from Observation

Human demonstration data is often ambiguous and incomplete, motivating imitation learning approaches that also exhibit reliable planning behavior. A common paradigm learns a reward function via Inverse Reinforcement Learning (IRL) and deploys it using Model Predictive Control (MPC) to reliably imitate expert behavior. In this work, we propose replacing the [...]

Wheeled Lab: Modern Sim2Real for Low-Cost, Open-Source Wheeled Robotics

Simulation has been pivotal in recent robotics milestones and is poised to play a prominent role in the field’s future. However, recent robotic advances often rely on expensive and high-maintenance platforms, limiting access to broader robotics audiences. This work introduces Wheeled Lab, a framework for the low-cost, open-source wheeled platforms [...]

Distributional Successor Features Enable Zero-Shot Policy Optimization

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. [...]

Model Predictive Control for Aggressive Driving over Uneven Terrain

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 [...]

Dynamics Models in the Aggressive Off-Road Driving Regime

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 [...]

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