Noah Wach, Manuel Rudolph, Fred Jendrzejewski, Sebastian Schmitt,
"Data Re-Uploading on with a single qudit",
Quantum Machine Intelligence, 2023.
Abstract
Quantum two-level systems, i.e. qubits, form the basis for most quantum machine learning approaches that have been proposed throughout the years. However, in some cases, higher dimensional quantum systems may prove to be advantageous. Here, we explore the capabilities of multi-level quantum systems, so-called qudits, for their use in a quantum machine learning context.
We formulate classification and regression problems with the data re-uploadi...
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Christiane Wiebel and Martina Hasenjäger,
"Exploring the Relationship Between Gaze and Movement Transitions During Natural Human Walking on Different Terrains",
44th European Conference on Visual Perception, 2023.
Abstract
Understanding and predicting human walk behavior is an important prerequisite for a proper design of physical assist robot control. One challenge for such systems is the accurate and timely prediction of walk transitions. To improve models based on gait behavior only, prior work has investigated the effect of exploiting visual sensor data. Only few works have included human visual behavior, even though gaze plays a significant role for successful...
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Thomas Weisswange, Joel Schwartz von Sproutel, Aaron Horowitz, Jens Schmüdderich,
"Telepresence Lantern - Designing an Immersive Video-Mediated Communication Device for Older Adults",
ArXiv.org, 2023.
Abstract
We present the “Telepresence Lantern” concept, developed to provide opportunities for older adults to stay in contact with remote
family and friends. It provides a new approach to video-mediated communication, designed to facilitate natural and ambient
interactions with simplified call setup. Video communication is an established way to enhance social connectedness, but
traditional approaches create a high friction ...
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Nikolas Hohmann, Sebastian Brulin, Jürgen Adamy, Markus Olhofer,
"Three-Dimensional Urban Path Planning for Aerial Vehicles Regarding Many Objectives",
IEEE Open Journal on Intelligent Transportation Systems, vol. 4, pp. 639-352, 2023.
Abstract
Planning flight paths for unmanned aerial vehicles in urban areas requires consideration of safety, legal, and economic aspects as well as attention to social factors for gaining public acceptance. To solve this many-objective path planning problem in the three-dimensional space, we propose a hybrid framework combining an exact Dijkstra search and a metaheuristic evolutionary optimization. Given a start and an endpoint, we optimize a path regardi...
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Patricia Wollstadt and Matti Krüger,
"Quantifying cooperation between artificial agents using synergistic information",
2022 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1044-1051, 2023.
Abstract
When designing interactive human-machine sys-
tems, it is often assumed that it is desirable for such systems
to behave cooperatively towards a human operator in order to
improve trust, acceptance, and usability, but also to increase
task effi ciency. To design cooperative human-machine interaction
(HMI) systems, we have to be able to defi ne and quantitatively
describe cooperative behavior, for example, to control, optimize,
or evaluate t...
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Anna Belardinelli, Chao Wang, Michael Gienger,
"Explainable Human-Robot Interaction for imitation learning in Augmented Reality",
16th International Workshop on Human-Friendly Robotics, 2023.
Abstract
Imitation learning could enable non-expert users to teach
new skills to robots in an interactive and intuitive way. Still, when teaching a task, it is often difficult to grasp what the robot knows or to assess
if a correct task representation is being formed. To address this problem, suitable online feedback should be given by the robot to explain its
perceptual beliefs.
Here, we introduce an explainable design for human-robot interaction
du...
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Mariusz Bujny, Muhammad Yousaf, Nate Zurbrugg, Duane Detwiler, Stefan Menzel, Satchit Ramnath, Thiago Rios, Fabian Duddeck,
"Learning Hyperparameter Predictors for Similarity-based Multidisciplinary Topology Optimization",
Scientific Reports, 2023.
Abstract
Topology optimization (TO) plays a significant role in industry by providing engineers with optimal material distributions based exclusively on the information about the design space and loading conditions. Such approaches are especially important for current multidisciplinary design tasks in industry, where the conflicting criteria often lead to very unintuitive solutions. Despite the progress in integrating manufacturing constraints into TO, on...
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Julian Eggert, Jörg Deigmöller, Pavel Smirnov, Johane Takeuchi, Andreas Richter,
"Memory Net: Generalizable Common-Sense Reasoning over Real-World Actions and Objects",
International Conference on Knowledge Engineering and Ontology Development, 2023.
Abstract
Abstract. We address the problem of situated reasoning of artificial agents (AA) in human-like environments. In particular, we want the AAs
to reason about so-called action patterns in a real-world human environment: E.g., which tools can be used for a certain action, which actions
can be performed with certain tools and objects, and so on. This should occur in a situated way, i.e., always referring to concrete instances in a
real-world enviro...
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Felix Ocker, Jörg Deigmöller, Julian Eggert,
"Exploring Large Language Models as a Source of Common-Sense Knowledge for Robots",
International Semantic Web Conference, 2023.
Abstract
By definition, service robots are supposed to help humans in everyday situations. To behave as expected, the robots require a sound knowledge base, allowing them to infer necessary actions. For situations such as serving a drink in the desired way, common-sense knowledge is required. The challenge with common-sense knowledge is that it is inherently implicit, i.e., it is self-evident for humans but not explicitly documented. Compared to the amoun...
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Jonathan Jakob, Martina Hasenjäger, Barbara Hammer,
"Incremental Human Gait Prediction without Catastrophic forgetting",
IEEE SSCI 2023, 2023.
Abstract
Human gait prediction is an important task in predictive exoskeleton control. However, if static models are used to facilitate this task, two problems arise. First, the models cannot adapt to new environments and terrains during deployment, and second, the models cannot be personalized to any given end user without costly involvement of a human expert. Incremental models can alleviate these shortcomings, but they usually are prone to catastrophic...
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