Developing capable robotic systems & understandable robot interactions

Zhao Han

Meet Zhao Han


Zhao Han is a Post-Doctoral Fellow of Computer Science at the Colorado School of Mines, joining Dr. Tom Williams’ interactive robotics MIRRORLab.

His research lies broadly in human-robot interaction (HRI), robotics, artificial intelligence (AI), and augmented reality (AR). More specifically, he focuses on designing and studying novel robotic systems and interactions, for embodied robots being more capable in human environments and more understandable while interacting with humans.

Dr. Han is an award-winning researcher, leading teams to win multiple robot competitions: first place in the Panasonic Prototype 3D LiDAR Challenge, second place in the FetchIt Mobile Manipulation Challenge at ICRA 2019, and a finalist in the Analog Devices (ADI) Real-Time Sensor Fusion Challenge. He has recently won the best late-breaking report third prize at HRI 2022.

Service is important and beneficial to academic life. He co-organizes the AI-HRI symposium, is actively involved in HRI standardization and more. At Mines, Dr. Zhao Han founded the Mines Asian Community Alliance and is a founding officer of Mines’ office of postdoctoral affairs. He believes in STEM outreach to broaden participation and involve under-represented, under-resourced communities.

Dr. Han received his Ph.D. (2021) in Computer Science from the University of Massachusetts Lowell in the Human-Robot Interaction Lab, directed by AAAI Fellow Dr. Holly Yanco. Previously, he received his M.S. (advised by Dr. Carson Leung in the Database and Data Mining Lab) and B.S. degrees in Computer Science at the University of Manitoba in Canada.

His Research


Human grasp effort is detected and the participant took the object with ease

Human-Robot Interaction
(HRI)

fetch large gear

Robotics

Robot Failure Explanation Algorithm with Behavior Trees

Artificial Intelligence
(AI)

nav path to

Augmented Reality
(AR)


Recent Publications


Evaluating Referring Form Selection Models in Partially-Known Environments
Zhao Han, Polina Rygina, Tom Williams

Evaluating Referring Form Selection Models in Partially-Known Environments

INLG 2022
Towards Formalizing HRI Data Collection Processes
Zhao Han and Tom Williams

Towards Formalizing HRI Data Collection Processes

HRI Methods & Metrics 2022
Towards an Understanding of Physical vs Virtual Robot Appendage Design
Zhao Han*, Albert Phan*, Amia Castro*, Fernando Sandoval Garza* and Tom Williams

Towards an Understanding of Physical vs Virtual Robot Appendage Design

VAM-HRI 2022
“Why Didn’t I Do It?” A Study Design to Evaluate Robot Explanations
Gregory LeMasurier, Alvika Gautam, Zhao Han, Jacob W. Crandall, Holly A. Yanco

“Why Didn’t I Do It?” A Study Design to Evaluate Robot Explanations

WYSD 2022
Causal Robot Communication Inspired by Observational Learning Insights
Zhao Han, Boyoung Kim, Holly A. Yanco, Tom Williams

Causal Robot Communication Inspired by Observational Learning Insights

AAAI-SSS 2022
A Task Design for Studying Referring Behaviors for Linguistic HRI
Zhao Han and Tom Williams

A Task Design for Studying Referring Behaviors for Linguistic HRI

HRI 2022 LBR

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