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cs.RO2025
GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems
Johannes A. Gaus, Junheon Yoon, Woo-Jeong Baek +3
This paper presents the framework \textbf{GUARD} (\textbf{G}uided robot control via \textbf{U}ncertainty attribution and prob\textbf{A}bilistic kernel optimization for \textbf{R}is…
cs.RO2025
LiPo: A Lightweight Post-optimization Framework for Smoothing Action Chunks Generated by Learned Policies
Dongwoo Son, Suhan Park
Recent advances in imitation learning have enabled robots to perform increasingly complex manipulation tasks in unstructured environments. However, most learned policies rely on di…