3 papers
cs.RO2026
Ordinal Neural Collapse as a Representation Prior for Visual Navigation
E-In Son, Jung-Taak Kim, Seung-Woo Seo
Learning robust navigation policies directly from visual observations remains a fundamental challenge in vision-based robotic navigation. In end-to-end imitation learning approache…
cs.RO2026
FPAS: Frontier-Based Path Planning with Adaptive Sampling for Large-Scale Unknown Environments
Jinwoo Choi, Yeonkyu Lee, Jung-Taak Kim +2
In this work, we propose Frontier-based Path Planning with Adaptive Sampling (FPAS), a novel framework designed for efficient goal-reaching in large-scale, unknown environments. Wh…
cs.RO2026
How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning
Hyung-Suk Yoon, Seung-Woo Seo
Distribution shift in imitation learning refers to the problem that the agent cannot plan proper actions for a state that has not been visited during the training. This problem can…