Dipl.-Inf.

Roscher, Karsten

Filters

5
5
4 1
Reset filters

Settings
Now showing 1 - 5 of 5
No Thumbnail Available
Publication

Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework

2023 , Schwaiger, Franziska , Matic-Flierl, Andrea , Roscher, Karsten , Günnemann, Stephan

The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications. Especially for detecting humans, which is often a fundamental task in safety-critical applications, it is vital to prevent errors. To address this challenge, we propose a self-monitoring framework that allows for the perception system to perform plausibility checks at runtime. We show that by incorporating an additional component for detecting human body parts, we are able to significantly reduce the number of missed human detections by factors of up to 9 when compared to a baseline setup, which was trained only on holistic person objects. Additionally, we found that training a model jointly on humans and their body parts leads to a substantial reduction in false positive detections by up to 50 percent compared to training on humans alone. We performed comprehensive experiments on the publicly available datasets DensePose and Pascal VOC in order to demonstrate the effectiveness of our framework.

View
No Thumbnail Available
Publication

Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models

2023 , Haider, Tom , Roscher, Karsten , Schmoeller da Roza, Felippe , Günnemann, Stephan

Reliability of reinforcement learning (RL) agents is a largely unsolved problem. Especially in situations that substantially differ from their training environment, RL agents often exhibit unpredictable behavior, potentially leading to performance loss, safety violations or catastrophic failure. Reliable decision making agents should therefore be able to cast an alert whenever they encounter situations they have never seen before and do not know how to handle. While the problem, also known as out-of-distribution (OOD) detection, has received considerable attention in other domains such as image classification or sensory data analysis, it is less frequently studied in the context of RL. In fact, there is not even a common understanding of what OOD actually means in RL. In this work, we want to bridge this gap and approach the topic of OOD in RL from a general perspective. For this, we formulate OOD in RL as severe perturbations of the Markov decision process (MDP). To detect such perturbations, we introduce a predictive algorithm utilizing probabilistic dynamics models and bootstrapped ensembles. Since existing benchmarks are sparse and limited in their complexity, we also propose a set of evaluation scenarios with OOD occurrences. A detailed analysis of our approach shows superior detection performance compared to existing baselines from related fields.

View
No Thumbnail Available
Publication

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution

2023 , Franco, Nicola , Korth, Daniel , Lorenz, Jeanette Miriam , Roscher, Karsten , Günnemann, Stephan

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to make a confident prediction. In this study, we present a novel approach for certifying the robustness of OOD detection within a ℓ2-norm around the input, regardless of network architecture and without the need for specific components or additional training. Further, we improve current techniques for detecting adversarial attacks on OOD samples, while providing high levels of certified and adversarial robustness on in-distribution samples. The average of all OOD detection metrics on CIFAR10/100 shows an increase of ∼ 13%/5% relative to previous approaches. Code: https://github.com/FraunhoferIKS/distro

View
No Thumbnail Available
Publication

Safe and Efficient Operation with Constrained Hierarchical Reinforcement Learning

2023 , Schmoeller da Roza, Felippe , Roscher, Karsten , Günnemann, Stephan

Hierarchical Reinforcement Learning (HRL) holds the promise of enhancing sample efficiency and generalization capabilities of Reinforcement Learning (RL) agents by leveraging task decomposition and temporal abstraction, which aligns with human reasoning. However, the adoption of HRL (and RL in general) to solve problems in the real world has been limited due to, among other reasons, the lack of effective techniques that make the agents adhere to safety requirements encoded as constraints, a common practice to define the functional safety of safety-critical systems. While some constrained Reinforcement Learning methods exist in the literature, we show that regular flat policies can face performance degradation when dealing with safety constraints. To overcome this limitation, we propose a constrained HRL topology that separates planning and control, with constraint optimization achieved at the lower-level abstraction. Simulation experiments show that our approach is able to keep its performance while adhering to safety constraints, even in scenarios where the flat policy’s performance deteriorates when trying to prioritize safety.

View
No Thumbnail Available
Publication

Towards Probabilistic Safety Guarantees for Model-Free Reinforcement Learning

2023 , Schmoeller da Roza, Felippe , Roscher, Karsten , Günneman, Stephan

Improving safety in model-free Reinforcement Learning is necessary if we expect to deploy such systems in safety-critical scenarios. However, most of the existing constrained Reinforcement Learning methods have no formal guarantees for their constraint satisfaction properties. In this paper, we show the theoretical formulation for a safety layer that encapsulates model epistemic uncertainty over a distribution of constraint model approximations and can provide probabilistic guarantees of constraint satisfaction.

View