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Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie Shah , "Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty", Research and Automation Letters, 2022.
AbstractConsistent state estimation is challenging, especially when both dynamic and observation models are nonlinear and learned from data. In this work, we develop a set-based estimation algorithm, that produces zonotopic state estimates that respect the epistemic uncertainties in the learned mod- els, in addition to the aleatoric uncertainties. Our algorithm guarantees probabilistic consistency, in the sense that the true state is always bound...
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Zhenpeng Shi, Nikolay Matyunin, Kalman György Graffi, David Starobinski , "Automatic Categorization of Products for Threat Modeling", IEEE Secure Development Conference 2022, 2022.
AbstractWe consider the problem of systematizing the creation of general product categories used by libraries of threat modeling tools. Leveraging connections between various threat databases (CPE, CVE, and CWE), we propose and evaluate a method based on clustering CPE entries with the help of a knowledge graph. This clustering method allows one to categorize products, and identify the characteristics of each category based on the associated weaknesses. ...
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Chao Wang and Anna Belardinelli , "Investigating explainable human-robot interaction with augmented reality", International Workshop on Virtual, Augmented, and Mixed-Reality for Human-Robot Interactions (VAM@HRI2022), 2022.
AbstractIn learning by demonstration with social robots, a fluid and coordinated interaction between human teacher and robotic learner is particularly critical and yet often difficult to assess. This is even more the case, if robots are to learn from non-expert users. In such cases, it is sometimes troublesome for the teacher to get a grasp of what the robot knows or to assess if a correct representation of the task has been formed even before the robot ...
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Anna Belardinelli , "Estimating manipulation intentions to ease teleoperation", AIhub website (https://aihub.org/), 2022.
AbstractNo abstract...
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Muhammad Haris, Mathias Franzius, Ute Bauer-Wersing , " Learning Visual Landmarks for Localization with Minimal Supervision", International Conference on IMAGE ANALYSIS AND PROCESSING, pp. 773-786, 2022.
AbstractCamera localization is one of the fundamental requirements for vision-based mobile robots, self-driving cars, and augmented reality applications. In this context, learning spatial representations relative to unique regions in a scene with Slow Feature Analysis (SFA) has demonstrated large-scale localization. However, it relies either on pre-existing object detectors or hand-labeled data to train a CNN for recognizing unique regions in a scene. We...
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Michael Gienger , "Bi-Manual robot manipulation - concepts and experiences", Hybrid / USA: ICRA 2022 workshop talk: Bi-manual Manipulation: Addressing Real-world Challenges , 2022.
AbstractInvited talk at ICRA 2022 conference workshop...
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Timo Friedrich , "Three-Dimensional Voxel-Based Neural Style Transfer and Quantification", Bielefeld University, 2022.
AbstractMachine Learning and especially Deep Learning has started to conquer another human trait in recent years by being able to perform creative tasks. Neural Network based systems compose music, create dream-like creatures, generate faces of fictional persons, and even write complete books. Accordingly, of course, they also generate visual art. Here, Neural Style Transfer stylizes images and photographs with a style extracted from an arbitrary image, ...
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Radu Stoican, Angelo Cangelosi, Christian Goerick, Thomas H Weisswange , "Trust and Wellbeing in Transparent Human-Robot Interaction Teams", Centre for Robotics and AI Inaugural Conference, 2022.
AbstractMost research in human-robot interaction focuses on either the single-human case or the multi-human case where there is direct interaction between the robot and each human. The multi-human scenario in which some of the humans depend on the robot, but do not interact with it directly, is currently less studied. In this paper, we introduce a human-human-robot collaboration task, in which the robot interacts directly with only one of the humans. The...
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Viktor Losing and Julian Eggert , "Extraction of Common Physical Properties of Everyday Objects from Structured Sources", NLPIR '22: Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval, pp. 164-168, 2022.
AbstractCommonsense knowledge is essential for the reasoning of AI systems, particularly in the context of action planning for robots. The focus of this paper is on common-sense object properties, which are especially useful to restrict the search space of planning algorithms. Popular sources for such knowledge are commonsense knowledge bases that provide the information in a structured form. However, the utility of the provided object-property pairs is ...
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Pascal Lieser , "Determination of relevant Coorperation Partners by Machine Learning", TU Darmstadt, 2022.
AbstractIn this paper, the relevance of road users for a possible future interaction is investigated. For this purpose, the concept of interaction is first discussed. This is described from various scientific perspectives using different methods and terms. Subsequently, an overall definition for interaction in road traffic is explained. In this work, a data-driven approach is followed. Several datasets containing interactive traffic situations are availa...
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