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Matthias Schubert

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7 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Aerial Coverage Path Planning in Nuclear Emergencies

Johann Blake
Matthias Schubert

We formulate a Coverage Path Planning (CPP) problem for a helicopter or a UAV tasked with mapping ground-level radiation while avoiding radiation that is too strong. We introduce a simulation environment that incorporates digital elevation models, altitude-dependent measurement footprints and realistic flight constraints, as well as state-of-the-art radiation scenario simulations, such as nuclear explosions, provided by the German Federal Office for Radiation Protection. We highlight the complexity of radiological survey missions and demonstrate the necessity for new CPP approaches that address these unique challenges. The code to our simulation environment can be found under https: //github. com/JohannBlake/Aerial-Coverage-Path-Planning-in-Nuclear-Emergencies.

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NeurIPS Conference 2024 Conference Paper

Autoregressive Policy Optimization for Constrained Allocation Tasks

David Winkel
Niklas Strauß
Maximilian Bernhard
Zongyue Li
Thomas Seidl
Matthias Schubert

Allocation tasks represent a class of problems where a limited amount of resources must be allocated to a set of entities at each time step. Prominent examples of this task include portfolio optimization or distributing computational workloads across servers. Allocation tasks are typically bound by linear constraints describing practical requirements that have to be strictly fulfilled at all times. In portfolio optimization, for example, investors may be obligated to allocate less than 30\% of the funds into a certain industrial sector in any investment period. Such constraints restrict the action space of allowed allocations in intricate ways, which makes learning a policy that avoids constraint violations difficult. In this paper, we propose a new method for constrained allocation tasks based on an autoregressive process to sequentially sample allocations for each entity. In addition, we introduce a novel de-biasing mechanism to counter the initial bias caused by sequential sampling. We demonstrate the superior performance of our approach compared to a variety of Constrained Reinforcement Learning (CRL) methods on three distinct constrained allocation tasks: portfolio optimization, computational workload distribution, and a synthetic allocation benchmark. Our code is available at: https: //github. com/niklasdbs/paspo

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ECAI Conference 2024 Conference Paper

Context Matters: Leveraging Spatiotemporal Metadata for Semi-Supervised Learning on Remote Sensing Images

Maximilian Bernhard
Tanveer Hannan
Niklas Strauß
Matthias Schubert

Remote sensing projects typically generate large amounts of imagery that can be used to train powerful deep neural networks. However, the amount of labeled images is often small, as remote sensing applications generally require expert labelers. Thus, semi-supervised learning (SSL), i. e. , learning with a small pool of labeled and a larger pool of unlabeled data, is particularly useful in this domain. Current SSL approaches generate pseudo-labels from model predictions for unlabeled samples. As the quality of these pseudo-labels is crucial for performance, utilizing additional information to improve pseudo-label quality yields a promising direction. For remote sensing images, geolocation and recording time are generally available and provide a valuable source of information as semantic concepts, such as land cover, are highly dependent on spatiotemporal context, e. g. , due to seasonal effects and vegetation zones. In this paper, we propose to exploit spatiotemporal metainformation in SSL to improve the quality of pseudo-labels and, therefore, the final model performance. We show that directly adding the available metadata to the input of the predictor at test time degenerates the prediction quality for metadata outside the spatiotemporal distribution of the training set. Thus, we propose a teacher-student SSL framework where only the teacher network uses metainformation to improve the quality of pseudo-labels on the training set. Correspondingly, our student network benefits from the improved pseudo-labels but does not receive metadata as input, making it invariant to spatiotemporal shifts at test time. Furthermore, we propose methods for encoding and injecting spatiotemporal information into the model and introduce a novel distillation mechanism to enhance the knowledge transfer between teacher and student. Our framework dubbed Spatiotemporal SSL can be easily combined with several state-of-the-art SSL methods, resulting in significant and consistent improvements on the BigEarthNet and EuroSAT benchmarks. Code is available at https: //github. com/mxbh/spatiotemporal-ssl.

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AAAI Conference 2023 Conference Paper

InstanceFormer: An Online Video Instance Segmentation Framework

Rajat Koner
Tanveer Hannan
Suprosanna Shit
Sahand Sharifzadeh
Matthias Schubert
Thomas Seidl
Volker Tresp

Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on the whole video and the immense computational complexity caused by full Spatio-temporal attention limit them in real-life applications such as processing lengthy videos. In this paper, we propose a single-stage transformer-based efficient online VIS framework named InstanceFormer, which is especially suitable for long and challenging videos. We propose three novel components to model short-term and long-term dependency and temporal coherence. First, we propagate the representation, location, and semantic information of prior instances to model short-term changes. Second, we propose a novel memory cross-attention in the decoder, which allows the network to look into earlier instances within a certain temporal window. Finally, we employ a temporal contrastive loss to impose coherence in the representation of an instance across all frames. Memory attention and temporal coherence are particularly beneficial to long-range dependency modeling, including challenging scenarios like occlusion. The proposed InstanceFormer outperforms previous online benchmark methods by a large margin across multiple datasets. Most importantly, InstanceFormer surpasses offline approaches for challenging and long datasets such as YouTube-VIS-2021 and OVIS. Code is available at https://github.com/rajatkoner08/InstanceFormer.

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ECAI Conference 2023 Conference Paper

Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning

David Winkel
Niklas Strauß
Matthias Schubert
Thomas Seidl 0001

Portfolio optimization tasks describe sequential decision problems in which the investor’s wealth is distributed across a set of assets. Allocation constraints are used to enforce minimal or maximal investments into particular subsets of assets to control for objectives such as limiting the portfolio’s exposure to a certain sector due to environmental concerns. Although methods for (CRL) can optimize policies while considering allocation constraints, it can be observed that these general methods yield suboptimal results. In this paper, we propose a novel approach to handle allocation constraints based on a decomposition of the constraint action space into a set of unconstrained allocation problems. In particular, we examine this approach for the case of two constraints. For example, an investor may wish to invest at least a certain percentage of the portfolio into green technologies while limiting the investment in the fossil energy sector. We show that the action space of the task is equivalent to the decomposed action space, and introduce a new (RL) approach CAOSD, which is built on top of the decomposition. The experimental evaluation on real-world Nasdaq data demonstrates that our approach consistently outperforms state-of-the-art CRL benchmarks for portfolio optimization.

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IJCAI Conference 2020 Conference Paper

Semi-Markov Reinforcement Learning for Stochastic Resource Collection

Sebastian Schmoll
Matthias Schubert

We show that the task of collecting stochastic, spatially distributed resources (Stochastic Resource Collection, SRC) may be considered as a Semi-Markov-Decision-Process. Our Deep-Q-Network (DQN) based approach uses a novel scalable and transferable artificial neural network architecture. The concrete use-case of the SRC is an officer (single agent) trying to maximize the amount of fined parking violations in his area. We evaluate our approach on a environment based on the real-world parking data of the city of Melbourne. In small, hence simple, settings with short distances between resources and few simultaneous violations, our approach is comparable to previous work. When the size of the network grows (and hence the amount of resources) our solution significantly outperforms preceding methods. Moreover, applying a trained agent to a non-overlapping new area outperforms existing approaches.

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IJCAI Conference 2018 Conference Paper

Dynamic Resource Routing using Real-Time Dynamic Programming

Sebastian Schmoll
Matthias Schubert

Acquiring available resources in stochastic environments becomes more and more important to future mobility. For instance, cities like Melbourne, Canberra and San Francisco install sensors that detect in real-time whether a parking spot (resource) is available or not. In such environments, the current state of the resources may be fully observable, although the future development is stochastic. In order to reduce the traffic, such cities want to fully exploit parking spots, such that the amount of searching cars is minimized. Thus, we formulate a problem setting where the expected seek time for each driver is minimized. This problem can be modeled by a Markov Decision Process (MDP) and solved using standard algorithms. In this paper, we focus on the setting, where pre-computation is not possible and search policies have to be computed on the fly. Our approach is based on state-of-the-art Real-Time Dynamic Programming (RTDP) approaches. However, standard RTDP approaches do not perform well on this specific problem setting as shown in our experiments. We introduce adapted bounds and approximations that exploit the specific nature of the problem in order to improve the performance significantly.

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