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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Qiqi Liu, Ran Cheng, Yaochu Jin, Martin Heiderich, Tobias Rodemann,
"Reference Vector Assisted Adaptive Model Management for Surrogate-Assisted Many-objective Optimization",
IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022.
Abstract
Acquisition functions for surrogate-assisted many-objective optimization require a delicate balance between convergence and diversity. To meet this requirement, we propose
an adaptive model management strategy assisted by two sets of reference vectors, one set of adaptive reference vectors accounting for convergence while the other set of fixed reference vectors
for diversity. Specifically, we first propose a new acquisition function that calcu...
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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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Matih Ullah,
"Landmark Independent Visual Localization with Slow Feature Analysis",
University of Applied Sciences Frankfurt , 2022.
Abstract
Visual localization is an area of interest to research in mobile robots, self-driving cars, etc.
Localization using a camera is one of the fundamental requirements for a vision-based mobile
robot. In this context, unsupervised learning with Slow Feature Analysis (SFA) directly
applies to the images to extract a spatial representation of the environment. In the past, SFA is
used with Convolutional Neural Network (CNN) to...
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Meike Kühne, Tim Schrills, Markus Gödker, Patricia Wollstadt, Thomas Franke,
"Subjective Information Processing Awareness for Intelligent Charging Agents - Connecting Traceability, Trust & Users’ Ability to Predict",
DGPS Kongress 2022, Deutsche Gesellschaft fuer Psychologie, 2022.
Abstract
While interacting with artificial intelligence (AI), users experience automated information processing, which can remain untraceable to them. This involves evaluating options in the area of intelligent bidirectional charging of electric vehicles (EV). Untraceable information processing can have negative effects on the cooperation between humans and AI, since it will not be recognizable to humans according to which reference values specific chargi...
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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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Sneha Saha,
"Learning-based Generative Representations for Automotive Design Optimization",
University of Birmingham, 2022.
Abstract
In this thesis, we envisioned a cooperative design system (CDS) which learn from the existing 3D designs generated during past optimization cycles and is able to generate potential alternatives to assist designer's ideation process. The research in this thesis, address different aspects that can be combined to form a CDS framework. First, based on the survey of deep learning techniques, a point cloud variational autoencoder is adapted from the li...
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Thomas Schnürer, Malte Probst , Horst-Michael Groß,
"Utilizing Emergent, Task-Independent Knowledge Representations for Accelerated Task-Learning in Reinforcement Learning",
Fifth International Workshop on Intrinsically-Motivated Open-ended Learning, Max Planck Institute for Intelligent Systems,, no. 5, 2022.
Abstract
An intelligent agent in a complex environment will face a great number of diverse tasks. Rather than learning
task-specific representations, we aim to reuse learned aspects to drive the acquisition of new tasks by leveraging
previously learned abstract knowledge. Building on recent work that has introduced an inductive bias for explicit
knowledge separation, we explore the benefits of such separation for learning new tasks. With an environment...
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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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Thomas Jatschka, Tobias Rodemann, Guenther Raidl,
"A Large Neighborhood Search for a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications",
META2021 Conference, pp. 3-17, 2022.
Abstract
We present a large neighborhood search (LNS) as optimization core for a cooperative optimization approach (COA) to optimize locations of service points for mobility applications. COA is an iterative interactive algorithm in which potential customers can express preferences during the optimization. A machine learning component processes the feedback obtained from the customers. The learned information is then used in an optimization component to g...
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