4 papers
Intrinsic Contraction Theory and Stabilization of Probability Measures on the Wasserstein Space
Min Kim, Joshua D. Ibrahim, Soon-Jo Chung
Deterministic flows can contract Wasserstein distances between probability laws without being contractive in point-state space. We develop a differential contraction theory for pus…
Statistical Learning of Contractive Dynamical Representations for Composite Adaptive Control
Min Kim, José Leonardo Brenes, Fred Hadaegh +1
We present a representation-learning framework for composite adaptive tracking control under dynamically coupled disturbances. The framework connects classical disturbance-accommod…
Schur-Neural KF: Learned Schur-Consistent Corrections to the Extended Kalman Filter
Min Kim, Lianghao Cao, Soon-Jo Chung +1
We present Schur-Neural KF (SN-KF), a learning-based correction to the extended Kalman filter (EKF) that preserves the probabilistic conditioning interpretation of the EKF. The met…
SPINE: Bridging the Cyber-Physical Gap with Agentic AI
Minkyu Ham, Dongho Kim, Chan Lee +7
Foundation models give robots powerful high-level reasoning, yet turning that intelligence into reliable physical action remains difficult: roboticists must still align device driv…