4 papers
Connectivity-Aware Representations for Constrained Motion Planning via Multi-Scale Contrastive Learning
Suhyun Jeon, Yumin Lim, Woo-Jeong Baek +3
The objective of constrained motion planning is to connect start and goal configurations while satisfying task-specific constraints. Motion planning becomes inefficient or infeasib…
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…
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…
NODE IK: Solving Inverse Kinematics with Neural Ordinary Differential Equations for Path Planning
Suhan Park, Mathew Schwartz, Jaeheung Park
This paper proposes a novel inverse kinematics (IK) solver of articulated robotic systems for path planning. IK is a traditional but essential problem for robot manipulation. Recen…