Internal State
"Internal state" research encompasses diverse fields, focusing on representing and utilizing internal representations of systems to improve prediction, decision-making, and control. Current research emphasizes developing robust methods for estimating and tracking internal states across various domains, employing techniques like Bayesian methods, neural networks (including recurrent and transformer architectures), and Gaussian processes. These advancements have significant implications for diverse applications, including improved battery management, more efficient power grid control, enhanced AI safety, and more accurate predictions in areas like weather forecasting and legal speech transcription.
Papers
SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models
José Ignacio Olalde-Verano, Sascha Kirch, Clara Pérez-Molina, Sergio Martin
State- and context-dependent robotic manipulation and grasping via uncertainty-aware imitation learning
Tim R. Winter, Ashok M. Sundaram, Werner Friedl, Maximo A. Roa, Freek Stulp, João Silvério
AI for ERW Detection in Clearance Operations -- The State of Research
Björn Kischelewski, Gregory Cathcart, David Wahl, Benjamin Guedj