8 papers
Generative AI for Safe and Photorealistic Drone Light Shows
Pascal Reinhold, Alexander Gräfe, Sebastian Trimpe
Drone light shows are redefining aerial entertainment, yet their widespread adoption is bottlenecked by labor-intensive, manual animation. While generative AI promises an automated…
Local Preferential Bayesian Optimization
Johanna Menn, Miriam Kober, Paul Brunzema +2
Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function. Preferential…
All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning
Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele +2
Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data effic…
Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank +2
Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on fir…
Learning to Race in Minutes: Infoprop Dyna on the Mini Wheelbot
Devdutt Subhasish, Henrik Hose, Sebastian Trimpe
Reinforcement Learning (RL) has the potential to enable robots with fast, nonlinear, and unstable dynamics to reach the limits of their performance. However, most recent advances r…
Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertainty
Artur Eisele, Bernd Frauenknecht, Friedrich Solowjow +1
Safety remains an open problem in reinforcement learning (RL), especially during training. While safety filters are promising to address safe exploration, they are generally poorly…