most citedLearning-based Adaptive Control of Quadruped Robots for Active Stabilization on Moving Platforms

4 citations · 4 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO2026

HiPAN: Hierarchical Posture-Adaptive Navigation for Quadruped Robots in Unstructured 3D Environments

Jeil Jeong, Minsung Yoon, Seokryun Choi +3

Navigating quadruped robots in unstructured 3D environments poses significant challenges, requiring goal-directed motion, effective exploration to escape from local minima, and pos…

cs.RO2026

Phase-Aware Policy Learning for Skateboard Riding of Quadruped Robots via Feature-wise Linear Modulation

Minsung Yoon, Jeil Jeong, Sung-Eui Yoon

Skateboards offer a compact and efficient means of transportation as a type of personal mobility device. However, controlling them with legged robots poses several challenges for p…

cs.RO2026

DyGeoVLN: Infusing Dynamic Geometry Foundation Model into Vision-Language Navigation

Xiangchen Liu, Hanghan Zheng, Jeil Jeong +5

Vision-language Navigation (VLN) requires an agent to understand visual observations and language instructions to navigate in unseen environments. Most existing approaches rely on…

cs.RO20264 cited

Learning-based Adaptive Control of Quadruped Robots for Active Stabilization on Moving Platforms

Minsung Yoon, Heechan Shin, Jeil Jeong +1

A quadruped robot faces balancing challenges on a six-degrees-of-freedom moving platform, like subways, buses, airplanes, and yachts, due to independent platform motions and result…

cs.RO2026

Uncertainty-Aware Non-Prehensile Manipulation with Mobile Manipulators under Object-Induced Occlusion

Jiwoo Hwang, Taegeun Yang, Jeil Jeong +2

Non-prehensile manipulation using onboard sensing presents a fundamental challenge: the manipulated object occludes the sensor's field of view, creating occluded regions that can l…

cs.RO2025

Efficient Navigation Among Movable Obstacles using a Mobile Manipulator via Hierarchical Policy Learning

Taegeun Yang, Jiwoo Hwang, Jeil Jeong +2

We propose a hierarchical reinforcement learning (HRL) framework for efficient Navigation Among Movable Obstacles (NAMO) using a mobile manipulator. Our approach combines interacti…