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David Hyunchul Shim

10 accepted papers

2026

DINO-VO: A Feature-Based Visual Odometry Leveraging a Visual Foundation Model

ICRA 2026poster

Learning-based monocular visual odometry (VO) poses robustness, generalization, and efficiency challenges in robotics. Recent advances in visual foundation models, such as DINOv2, have improved robustness and generalization in various vision tasks, yet their integration in VO remains limited due to …

2025

SPIBOT: A Drone-Tethered Mobile Gripper for Robust Aerial Object Retrieval in Dynamic Environments

ICRA 2025

In real-world field operations, aerial grasping systems face significant challenges in dynamic environments due to strong winds, shifting surfaces, and the need to handle heavy loads. Particularly when dealing with heavy objects, the powerful propellers of the drone can inadvertently blow the target

Cited by 1SourceScholar
2024

Fly by Book: How to Train a Humanoid Robot to Fly an Airplane using Large Language Models

IROS 2024poster

A pilot needs to manipulate various gadgets in the cockpit based on vast knowledge of rules and procedures while verbally communicating with air traffic controllers. While precision manipulation in the cockpit during the flight is already a difficult task, a far more difficult thing is how to make a…

Cited by 1SourceScholar
2024

Skill Q-Network: Learning Adaptive Skill Ensemble for Mapless Navigation in Unknown Environments

IROS 2024poster

This paper focuses on the acquisition of mapless navigation skills within unknown environments. We introduce the Skill Q-Network (SQN), a novel reinforcement learning method featuring an adaptive skill ensemble mechanism. Unlike existing methods, our model concurrently learns a high-level skill deci…

Cited by 0SourceScholar
2024

TempFuser: Learning Agile, Tactical, and Acrobatic Flight Maneuvers Using a Long Short-Term Temporal Fusion Transformer

RA-L 2024

Dogfighting is a challenging scenario in aerial applications that requires a comprehensive understanding of both strategic maneuvers and the aerodynamics of agile aircraft. The aerial agent needs to not only understand tactically evolving maneuvers of fighter jets from a long-term perspective but al

Cited by 3SourceScholar
2021

Incorporating Multi-Context Into the Traversability Map for Urban Autonomous Driving Using Deep Inverse Reinforcement Learning

RA-L 2021

Autonomous driving in an urban environment with surrounding agents remains challenging. One of the key challenges is to accurately predict the traversability map that probabilistically represents future trajectories considering multiple contexts: inertial, environmental, and social. To address this,

Cited by 29SourceScholar
2018

Perception, Guidance, and Navigation for Indoor Autonomous Drone Racing Using Deep Learning

RA-L 2018

In autonomous drone racing, a drone is required to fly through the gates quickly without any collision. Therefore, it is important to detect the gates reliably using computer vision. However, due to the complications such as varying lighting conditions and gates seen overlapped, traditional image pr

Cited by 167SourceScholar
2016

EureCar turbo: A self-driving car that can handle adverse weather conditions

IROS 2016poster

Autonomous driving technology has made significant advances in recent years. In order for self-driving cars to become practical, they are required to operate safely and reliably even under adverse driving conditions. However, most current autonomous driving cars have only been shown to be operationa…

Cited by 39SourceScholar
2016

Toward autonomous aircraft piloting by a humanoid robot: Hardware and control algorithm design

IROS 2016poster

Unmanned aerial vehicles (UAVs) are now very popular for many applications such as surveillance and transport and they are typically constructed starting from the design process. However, it is very time consuming and require all new airworthiness process. In this paper, we aim to provide a novel fr…

Cited by 8SourceScholar