Author

M. Kim

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#machine learning Preprint Sep 2026

Statistical Learning of Contractive Dynamical Representations for Composite Adaptive Control

A statistically principled hard expectation-maximization procedure is introduced, with a Kalman smoother in the hard E-step, to identify dynamical representations of disturbance whose latent evolution is uniformly contractive, which yields a composite adaptive tracking controller with predictive capability and provable...

Min Kim, José Leonardo Brenes, Fred Y. Hadaegh et al. · 0 citations

#artificial intelligence Jun 2026

SPINE: Bridging the Cyber-Physical Gap with Agentic AI

Results show that structured agentic debugging can address a key cyber-physical integration bottleneck in real-world robot deployment, and are shown to be more complete and efficient recovery than human operators using Claude Code.

Minkyu Ham, Dongho Kim, Chan Lee et al. · arXiv.org · 0 citations

Session H Abstracts Actor-critic reinforcement learning for model predictive control for the Autonomous Indy Car Challenge

This project implements AC4MPC, in which a reinforcement-learning agent is trained on the exact cost a model predictive controller optimizes, and its learned value function (critic) is embedded in the MPC as a terminal cost.

Ettienne Caban-Klepac, Soon-Jo Chung, Gilchrist X. Johnson et al. · 0 citations