147 citations · 590 across the 30 of their papers we have counts for
9 papers · 1 filter
Safe Reinforcement Learning with Probabilistic Guarantees Satisfying Temporal Logic Specifications in Continuous Action Spaces
Hanna Krasowski, Prithvi Akella, Aaron D. Ames +1
Vanilla Reinforcement Learning (RL) can efficiently solve complex tasks but does not provide any guarantees on system behavior. To bridge this gap, we propose a three-step safe RL…
Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes
Niklas Kochdumper, Hanna Krasowski, Xiao Wang +2
While reinforcement learning produces very promising results for many applications, its main disadvantage is the lack of safety guarantees, which prevents its use in safety-critica…
Fully-Automated Verification of Linear Systems Using Inner- and Outer-Approximations of Reachable Sets
Mark Wetzlinger, Niklas Kochdumper, Stanley Bak +1
Reachability analysis is a formal method to guarantee safety of dynamical systems under the influence of uncertainties. A substantial bottleneck of all reachability algorithms is t…
Model Predictive Robustness of Signal Temporal Logic Predicates
Yuanfei Lin, Haoxuan Li, Matthias Althoff
The robustness of signal temporal logic not only assesses whether a signal adheres to a specification but also provides a measure of how much a formula is fulfilled or violated. Th…
Timor Python: A Toolbox for Industrial Modular Robotics
Jonathan Külz, Matthias Mayer, Matthias Althoff
Modular Reconfigurable Robots (MRRs) represent an exciting path forward for industrial robotics, opening up new possibilities for robot design. Compared to monolithic manipulators,…
Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopes
Niklas Kochdumper, Christian Schilling, Matthias Althoff +1
We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic tangent activation functions.…
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments
Jakob Thumm, Matthias Althoff
Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as h…
Contingency-constrained economic dispatch with safe reinforcement learning
Michael Eichelbeck, Hannah Markgraf, Matthias Althoff
Future power systems will rely heavily on micro grids with a high share of decentralised renewable energy sources and energy storage systems. The high complexity and uncertainty in…
CoBRA: A Composable Benchmark for Robotics Applications
Matthias Mayer, Jonathan Külz, Matthias Althoff
Selecting an optimal robot, its base pose, and trajectory for a given task is currently mainly done by human expertise or trial and error. To evaluate automatic approaches to this…