52 citations · 52 across the 6 of their papers we have counts for
8 papers
Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model
Olayiwola Arowolo, Oluwadara Adedeji, Peter Palensky +1
Foundation models have shown strong generalization across scientific domains, but their use in power systems remains largely task-specific. We investigate whether a pretrained tabu…
Time-series Foundation Models for Predictive Control: The Role of Excitation
Mazen Amria, Jasper Hoffmann, Philipp Bordne +5
Deploying model predictive control (MPC) requires constructing or identifying a predictive model for each target system. Time-series foundation models (TSFMs) offer an attractive o…
Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction
Daniel Jost, Luca Paparusso, Martin Stoll +3
In highly interactive driving scenes, trajectory prediction is conditioned on information from surrounding traffic participants such as cars and pedestrians. Our main contribution…
Don't double it: Efficient Agent Prediction in Occlusions
Anna Rothenhäusler, Markus Mazzola, Andreas Look +2
Occluded traffic agents pose a significant challenge for autonomous vehicles, as hidden pedestrians or vehicles can appear unexpectedly, yet this problem remains understudied. Exis…
Revisiting Safe Exploration in Safe Reinforcement learning
David Eckel, Baohe Zhang, Joschka Bödecker
Safe reinforcement learning (SafeRL) extends standard reinforcement learning with the idea of safety, where safety is typically defined through the constraint of the expected cost…
Constrained Reinforcement Learning for Safe Heat Pump Control
Baohe Zhang, Lilli Frison, Thomas Brox +1
Constrained Reinforcement Learning (RL) has emerged as a significant research area within RL, where integrating constraints with rewards is crucial for enhancing safety and perform…
Constrained Reinforcement Learning with Smoothed Log Barrier Function
Baohe Zhang, Yuan Zhang, Lilli Frison +2
Reinforcement Learning (RL) has been widely applied to many control tasks and substantially improved the performances compared to conventional control methods in many domains where…
Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning
Torsten Koller, Felix Berkenkamp, Matteo Turchetta +2
Reinforcement learning has been successfully used to solve difficult tasks in complex unknown environments. However, these methods typically do not provide any safety guarantees du…