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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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Blog Post number 1
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portfolio
Extending Goal-Oriented Specification for Deep Reinforcement Learning to High-Dimensional State Spaces
This work extends goal-oriented specification for deep reinforcement learning to high-dimensional state spaces by introducing an intuitive feature space for defining goals independently of raw state representations. It also adds a dedicated training space, that is constructed from one or both representations, improving training in complex environments. Case studies in Pendulum and Pong show that the approach enables effective goal-oriented specification beyond low-dimensional settings.
Expansion of a real-time localization system and implementation of non-destructive testing methods for a mobile measurement robot
This work presents a mobile inspection robot for automated concrete-surface assessment using impulse-radar non-destructive testing to measure concrete cover. It extends and validates a real-time UWB localization system based on TDoA for indoor and outdoor use, demonstrating positional accuracy, trueness, and repeatability for reliable robot localization.
publications
Air-Coupled Broadband Impact-Echo Actuation Using Supersonic Jet Flow
Published in Journal of Nondestructive Evaluation, 2024
This paper is on a new approach for impact echo with air-coupled actuation based on supersonic jet flow
Recommended citation: Air-Coupled Broadband Impact-Echo Actuation Using Supersonic Jet Flow; Christoph Strangfeld, Bjarne Grotelüschen, Benjamin Bühling; In: Journal of Nondestructive Evaluation 43, 45 (2024)
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Extending Goal-Oriented Specification for Reinforcement Learning to High Dimensions
Published in In book: Modeling Decisions for Artificial Intelligence (pp.262-274)Publisher: Springer Nature, 2024
This paper extends goal-oriented specification by enabling training from high-dimensional state spaces while preserving the intuitive feature space used for goal specification.
Recommended citation: Extending Goal-Oriented Specification for Reinforcement Learning to High Dimensions; Bjarne Grotelüschen, Simon Schwan and Sabine Glesner; In book: Modeling Decisions for Artificial Intelligence (pp.262-274)Publisher: Springer Nature
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talks
Extending Goal-Oriented Specification for Reinforcement Learning to High Dimensions
Published:
Presentation of the Conference Paper “Extending Goal-Oriented Specification for Reinforcement Learning to High Dimensions”