Learning Based
Learning-based approaches are revolutionizing various fields by enabling systems to learn complex behaviors and adapt to dynamic environments, primarily aiming to improve efficiency, robustness, and safety. Current research focuses on applying deep reinforcement learning, diffusion models, and Koopman operators to control robots, optimize planning algorithms (like those for pathfinding and task sequencing), and improve the accuracy and efficiency of simulations. These advancements have significant implications for robotics, autonomous systems, and other domains requiring adaptable and intelligent control, offering solutions to challenges in areas such as safe navigation, precise manipulation, and efficient resource allocation.
Papers
Gaussian Process-Based Learning Control of Underactuated Balance Robots with an External and Internal Convertible Modeling Structure
Feng Han, Jingang Yi
In vivo learning-based control of microbial populations density in bioreactors
Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore, Giovanni Russo, Mario di Bernardo
Optimized Control Invariance Conditions for Uncertain Input-Constrained Nonlinear Control Systems
Lukas Brunke, Siqi Zhou, Mingxuan Che, Angela P. Schoellig
Deep learning-based estimation of time-dependent parameters in Markov models with application to nonlinear regression and SDEs
Andrzej Kałuża, Paweł M. Morkisz, Bartłomiej Mulewicz, Paweł Przybyłowicz, Martyna Wiącek
A Deep Learning-Based System for Automatic Case Summarization
Minh Duong, Long Nguyen, Yen Vuong, Trong Le, Ha-Thanh Nguyen
Readout Guidance: Learning Control from Diffusion Features
Grace Luo, Trevor Darrell, Oliver Wang, Dan B Goldman, Aleksander Holynski
Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters
Aksel Vaaler, Svein Jostein Husa, Daniel Menges, Thomas Nakken Larsen, Adil Rasheed