9 citations · 28 across the 23 of their papers we have counts for
25 papers
ROS2SmolVLA: Enabling Small Vision-Language-Action Models for Integration into Industrial-Grade Lightweight Robots
Nils Mandischer, Noah Böckmann, Ludwig Holl +1
Industrial demand changes the paradigms of production. Due to smaller batch sizes and more variations in products, companies face a growing challenge to adopt more adaptive product…
Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
Helena Stegherr, Michael Heider, Nils Meyer +7
Evolutionary computation offers a variety of tools to solve complex real-world optimization problems. However, research often focuses on smaller, simplified problems and optimizati…
Persona-Based Process Design for Assistive Human-Robot Workplaces for Persons with Disabilities
Nils Mandischer, Daria Eckert and, Lars Mikelsons
Human-robot interaction is emerging as an important paradigm for integrating persons with disabilities into the workplace. While these systems can enable individuals to work, their…
Assistive Robots and Reasonable Work Assignment Reduce Perceived Stigma toward Persons with Disabilities
Stina Klein, Birgit Prodinger, Elisabeth André +2
Robots are becoming more prominent in assisting persons with disabilities (PwD). Whilst there is broad consensus that robots can assist in mitigating physical impairments, the exte…
GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation
Phillip Mueller, Talip Uenlue, Sebastian Schmidt +4
Precise geometric control in image generation is essential for engineering \& product design and creative industries to control 3D object features accurately in image space. Tradit…
Holistic Specification of the Human Digital Twin: Stakeholders, Users, Functionalities, and Applications
Nils Mandischer, Alexander Atanasyan, Ulrich Dahmen +3
The digital twin of humans is a relatively new concept. While many diverse definitions, architectures, and applications exist, a clear picture is missing on what, in fact, makes a…
Conjugated Capabilities: Interrelations of Elementary Human Capabilities and Their Implication on Human-Machine Task Allocation and Capability Testing Procedures
Nils Mandischer, Larissa Füller, Torsten Alles +2
Human and automation capabilities are the foundation of every human-autonomy interaction and interaction pattern. Therefore, machines need to understand the capacity and performanc…
Beyond Features: How Dataset Design Influences Multi-Agent Trajectory Prediction Performance
Tobias Demmler, Jakob Häringer, Andreas Tamke +3
Accurate trajectory prediction is critical for safe autonomous navigation, yet the impact of dataset design on model performance remains understudied. This work systematically exam…
Masked Conditioning for Deep Generative Models
Phillip Mueller, Jannik Wiese, Sebastian Mueller +1
Datasets in engineering domains are often small, sparsely labeled, and contain numerical as well as categorical conditions. Additionally. computational resources are typically limi…
Dynamic Intent Queries for Motion Transformer-based Trajectory Prediction
Tobias Demmler, Lennart Hartung, Andreas Tamke +4
In autonomous driving, accurately predicting the movements of other traffic participants is crucial, as it significantly influences a vehicle's planning processes. Modern trajector…
MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling
Damian Boborzi, Phillip Mueller, Jonas Emrich +3
Generative models have recently made remarkable progress in the field of 3D objects. However, their practical application in fields like engineering remains limited since they fail…
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
Julius Aka, Johannes Brunnemann, Jörg Eiden +2
Variational Autoencoders (VAEs) are a powerful framework for learning latent representations of reduced dimensionality, while Neural ODEs excel in learning transient system dynamic…
GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design
Phillip Mueller, Sebastian Mueller, Lars Mikelsons
We provide a dataset for enabling Deep Generative Models (DGMs) in engineering design and propose methods to automate data labeling by utilizing large-scale foundation models. GeoB…
Perspectives-Observer-Transparency -- A Novel Paradigm for Modelling the Human in Human-To-Anything Interaction Based on a Structured Review of the Human Digital Twin
Nils Mandischer, Alexander Atanasyan, Michael Schluse +2
Modern modelling approaches fail when it comes to understanding rather than pure supervision of human behavior. As humans become more and more integrated into human-to-anything int…
Exploring Capability-Based Control Distributions of Human-Robot Teams Through Capability Deltas: Formalization and Implications
Nils Mandischer, Marcel Usai, Frank Flemisch +1
The implicit assumption that human and autonomous agents have certain capabilities is omnipresent in modern teaming concepts. However, none formalize these capabilities in a flexib…
InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture
Phillip Mueller, Jannik Wiese, Ioan Craciun +1
Recent advancements in image synthesis are fueled by the advent of large-scale diffusion models. Yet, integrating realistic object visualizations seamlessly into new or existing ba…
Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception
Phillip Mueller, Lars Mikelsons
The synthesis of product design concepts stands at the crux of early-phase development processes for technical products, traditionally posing an intricate interdisciplinary challen…
Learnable & Interpretable Model Combination in Dynamical Systems Modeling
Tobias Thummerer, Lars Mikelsons
During modeling of dynamical systems, often two or more model architectures are combined to obtain a more powerful or efficient model regarding a specific application area. This co…
Towards Consistent and Explainable Motion Prediction using Heterogeneous Graph Attention
Tobias Demmler, Andreas Tamke, Thao Dang +2
In autonomous driving, accurately interpreting the movements of other road users and leveraging this knowledge to forecast future trajectories is crucial. This is typically achieve…
Analyzing the coupling process of distributed mixed real-virtual prototypes
Peter Baumann, Lars Mikelsons, Oliver Kotte +1
The ongoing connection and automation of vehicles leads to a closer interaction of the individual vehicle components, which demands for consideration throughout the entire developm…
Eigen-informed NeuralODEs: Dealing with stability and convergence issues of NeuralODEs
Tobias Thummerer, Lars Mikelsons
Using vanilla NeuralODEs to model large and/or complex systems often fails due two reasons: Stability and convergence. NeuralODEs are capable of describing stable as well as instab…
NeuralFMU: Presenting a workflow for integrating hybrid NeuralODEs into real world applications
Tobias Thummerer, Johannes Stoljar, Lars Mikelsons
The term NeuralODE describes the structural combination of an Artifical Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODEs), the former acts as t…
Imitation Learning by State-Only Distribution Matching
Damian Boborzi, Christoph-Nikolas Straehle, Jens S. Buchner +1
Imitation Learning from observation describes policy learning in a similar way to human learning. An agent's policy is trained by observing an expert performing a task. While many…
Hybrid modeling of the human cardiovascular system using NeuralFMUs
Tobias Thummerer, Johannes Tintenherr, Lars Mikelsons
Hybrid modeling, the combination of first principle and machine learning models, is an emerging research field that gathers more and more attention. Even if hybrid models produce f…