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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.

Abstract

In 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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Muhammad Haris, Mathias Franzius, Ute Bauer-Wersing, " Learning Visual Landmarks for Localization with Minimal Supervision", International Conference on IMAGE ANALYSIS AND PROCESSING, 2022.

Abstract

Camera 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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Timo Friedrich, "Three-Dimensional Voxel-Based Neural Style Transfer and Quantification", Bielefeld University, 2022.

Abstract

Machine 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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Fabio Muratore, Fabio Ramos, Wenhao Yu, Greg Turk, Michael Gienger, Jan Peters, "Robot Learning from Randomized Simulations: A Review", Frontiers Robotics and AI, 2022.

Abstract

The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require giant amounts of data. It is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the art approaches learn in simulation where data generation is fast as well as inexpensive, and subsequently transfer the knowledge to the real robot sim-to-real. Despite becoming more and more realistic, all simulators...



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Muhammad Haris, Mathias Franzius, Ute Bauer-Wersing, "Physical Interactive Localization Learning ", 2022 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), 2022.

Abstract

Localization is fundamental for mobile robots, especially in unconstrained outdoor environments. Earlier work showed unsupervised localization learning on landmarks to be suitable for large-scale scenes. However, this relied on hand-labeled data to train a CNN for recognizing landmarks. We propose a new approach that allows a robot to learn landmarks for localization with a human cooperatively. This approach uses pre-trained detectors of c...



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Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck, "Evaluation of geometric similarity metrics for structural clusters generated using topology optimization", Applied Intelligence, 2022.

Abstract

In an engineering design process, multitudes of feasible designs can be automatically generated using structural optimization methods by varying the design requirements or user preferences for different performance objectives. Design exploration of such potentially large datasets is a challenging task. An unsupervised data-centric approach for exploring designs is to find clusters of similar designs and recommend only the cluster representat...



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Simon Kohaut, "Hybrid Probabilistic Logic Programming for Mission Design in Multimodal Mobility", Technical University of Darmstadt, 2022.

Abstract

Reasoning on subjective observations of the environment to navigate through complex and dynamic scenarios is a fundamental concept to human behavior. Hence, over the course of history, a vast landscape of approaches to formalize and automize inference has emerged. From propositional to higher-order logic and from simple stochastic measures to robust statistics, the power of both symbolic and numeric methods for reasoning in discrete and continuou...



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Kyle Poland, Anja Sturm, Aaron Gutknecht, Patricia Wollstadt, Michael Wibral, Abdullah Makkeh, "On a differentiable partial information decomposition for continuous random variables and applications in (artificial) neural networks", Bernstein Conference 2021, 2021.

Abstract

Understanding information mechanisms inside complex systems often poses intricate questions. In neural systems, information is often represented by an ensemble of agents. Knowledge about how information is distributed amongst those agents can lead to insights about how to distribute relevant information about a problem over available agents. These agents can, for instance, be neurons that are recorded during stimulation, one may imagine spike tra...



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Thomas Schmitt, Tobias Rodemann, Jürgen Adamy, "Automatized Decision Making in Multi-Objective MPC with Preferences", GMA Fachausschuss 1.40 „Systemtheorie und Regelungstechnik", 2021.

Abstract

If multiple objectives have to be considered in Model Predictive Control (MPC), usually this is achieved by using a weighted sum as the cost function of the optimal control problem. where the weights are fixed. However, if the circumstances vary over time, the selected weighting between the objectives might not be desirable anymore. Thus, concepts from Multi-Objective Optimization (MOO) can be used. In MOO, the main goal is to choose the Pareto...



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Samuele Vinanzi, Christian Goerick, Angelo Cangelosi, "The Collaborative Mind: Intention Reading and Trust in Human-Robot Interaction", iScience Special Issue, vol. 24, no. 2, 2021.

Abstract

Robots stand at the heart of a techno-scientific revolution which promises to significantly alter the way in which we conceive our society. Recent discoveries point towards a future in which artificial agents will become fully integrated in our social structures, thus becoming important actors in our everyday life. In this scenario, it is of critical importance for these robots to understand us in the most human-like fashion and to be able to ...



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