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Byeongho Yu

9 accepted papers

2026

LVI-Q: Robust LiDAR-Visual-Inertial-Kinematic Odometry for Quadruped Robots Using Tightly-Coupled and Efficient Alternating Optimization

Kevin Christiansen Marsim, Minho Oh, Byeongho Yu, Seungjae Lee, I Made Aswin Nahrendra, Hyungtae Lim +1
ICRA 2026poster

Autonomous navigation for legged robots in complex and dynamic environments relies on robust simultaneous localization and mapping (SLAM) systems to accurately map surroundings and localize the robot, ensuring safe and efficient operation. While prior sensor fusion-based SLAM approaches have integra…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

DreamFLEX: Learning Fault-Aware Quadrupedal Locomotion Controller for Anomaly Situation in Rough Terrains

Seunghyun Lee, I Made Aswin Nahrendra, Dongkyu Lee, Byeongho Yu, Minho Oh, Hyeonwoo Lee +1
ICRA 2025

Recent advances in quadrupedal robots have demonstrated impressive agility and the ability to traverse diverse terrains. However, hardware issues, such as motor overheating or joint locking, may occur during long-distance walking or traversing through rough terrains leading to locomotion failures. A

Cited by 3Source ↗Scholar ↗
2025

LVI-Q: Robust LiDAR-Visual-Inertial-Kinematic Odometry for Quadruped Robots Using Tightly-Coupled and Efficient Alternating Optimization

Kevin Christiansen Marsim, Minho Oh, Byeongho Yu, Seungjae Lee, I Made Aswin Nahrendra, Hyungtae Lim +1
RA-L 2025

Autonomous navigation for legged robots in complex and dynamic environments relies on robust simultaneous localization and mapping (SLAM) systems to accurately map surroundings and localize the robot, ensuring safe and efficient operation. While prior sensor fusion-based SLAM approaches have integra

Cited by 3Source ↗Scholar ↗
2025

PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality

Byeongho Yu, Changhun Lee, Jun-gyu Jin, Eunhyeok Park
EMNLP 2025

To mitigate the hallucination problem in large language models, DoLa exploits early exit logits from the same model as a contrastive prior. However, we found that these early exit logits tend to be flat, low in magnitude, and fail to reflect meaningful contrasts. To address this, we propose PruneCD,

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

TRG-Planner: Traversal Risk Graph-Based Path Planning in Unstructured Environments for Safe and Efficient Navigation

Dongkyu Lee, I Made Aswin Nahrendra, Minho Oh, Byeongho Yu, Hyun Myung
RA-L 2025

Unstructured environments such as mountains, caves, construction sites, or disaster areas are challenging for autonomous navigation because of terrain irregularities. In particular, it is crucial to plan a path to avoid risky terrain and reach the goal quickly and safely. In this paper, we propose a

Cited by 18Source ↗code ↗Scholar ↗
2023

DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning

I Made Aswin Nahrendra, Byeongho Yu, Hyun Myung
ICRA 2023poster

Quadrupedal robots resemble the physical ability of legged animals to walk through unstructured terrains. However, designing a controller for quadrupedal robots poses a significant challenge due to their functional complexity and requires adaptation to various terrains. Recently, deep reinforcement…

Cited by 106Source ↗Scholar ↗
2022

Retro-RL: Reinforcing Nominal Controller With Deep Reinforcement Learning for Tilting-Rotor Drones

I Made Aswin Nahrendra, Christian Tirtawardhana, Byeongho Yu, Eungchang Mason Lee, Hyun Myung
RA-L 2022

Studies that broaden drone applications into complex tasks require a stable control framework. Recently, deep reinforcement learning (RL) algorithms have been exploited in many studies for robot control to accomplish complex tasks. Unfortunately, deep RL algorithms might not be suitable for being de

Cited by 13Source ↗Scholar ↗
2022

STEP: State Estimator for Legged Robots Using a Preintegrated Foot Velocity Factor

Yeeun Kim, Byeongho Yu, Eungchang Mason Lee, Joon-Ha Kim, Hae-Won Park, Hyun Myung
RA-L 2022

Wepropose a novel state estimator for legged robots, <i>STEP</i>, achieved through a novel preintegrated foot velocity factor. In the preintegrated foot velocity factor, the usual non-slip assumption is not adopted. Instead, the end effector velocity becomes observable by exploiting the body speed o

Cited by 39Source ↗Scholar ↗
2021

CAROS-Q: Climbing Aerial RObot System Adopting Rotor Offset With a Quasi-Decoupling Controller

Hyungyu Lee, Byeongho Yu, Christian Tirtawardhana, Chanyoung Kim, Myeongwoo Jeong, Sumin Hu +1
RA-L 2021

Unmanned Aerial Vehicles (UAVs) have continually proven their effectiveness in various fields. However, UAVs have not yet matured enough to be used for vertical surface maintenance tasks, such as building inspection or cleaning. To mitigate this issue, this letter proposes a novel design for a coaxi

Cited by 20Source ↗Scholar ↗