Task-Oriented Hash-structured Occupancy Grid Mapping for efficient planning.
Research Interests
I believe that computational modeling of physics and scene understanding is key to developing robots with genuine physical understanding. Most of my research begins with: building real robots, developing perceptual systems that interpret the world, simulating their dynamics, and controlling them in the wild.
Representative papers are highlighted. (Equal contributions are denoted by *.)
Selected Publications
Disentangling context vectors in meta-learning enables robust and generalizable regression and locomotion across diverse tasks.
Developing a general trajectory tracking controller for a flying squirrel drone by learning wing dynamics and coordinating thrust–wing control for agile maneuvers.
Vision-based control of a full-sized quadruped robot in complex outdoor terrain using a perception pipeline and learned locomotion policies.
Building and controlling a highly nonlinear flying-squirrel-inspired quadrotor using reinforcement learning to manage foldable wings and thrust in challenging maneuvers.
A dual-unit wall-climbing robot that can adhere to ceilings and traverse obstacles using a passive hinge under kinodynamic constraints.
Project Highlights
Control & Physics simulation
Perception & Navigation
Hardware
Media & Press
I’ve had the opportunity to outreach & demonstrate my works to public.
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Flying squirrel drone
Informal interview from IEEE Spectrum (Evan Ackerman), New atlas (Cover article), Tech Xplore (Cover article), SBS (Korea major press), YTN (Korea major press), and many others.
Robot demonstrations
I’ve had the opportunity to demonstrate robots running my controllers in front of leading researchers and government leadership, presenting both the technical contributions and real-world performance.Service
Reviewer for IROS 2024, IROS 2025, CoRL 2025.