Automated Individualization of Object Detectors for the Semantic Environment Perception of Mobile Robots - Hofmann, Christian; May, Christopher; Ziegler, Patrick; Ghotbiravandi, Iliya; Franke, Jörg; Reitelshöfer, Sebastian (Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2025)
Large Language Models (LLMs) and Vision Language Models (VLMs) enable robots to perform complex tasks. However, many of today’s mobile robots cannot carry the computing hardware required to run these models on board. Furthermore, access via communication systems to external computers running these models is often impractical. Therefore, lightweight object detection models are often utilized to enable mobile robots to semantically perceive their environment. In addition, mobile robots are used in different environments, which also change regularly. Thus, an automated adaptation of object detectors would simplify the deployment of mobile robots. In this paper, we present a method for automated environment-specific individualization and adaptation of lightweight object detectors using LLMs and VLMs, which includes the automated identification of relevant object classes. We comprehensively evaluate our method and show its successful application in principle, while also pointing out shortcomings regarding semantic ambiguities and the application of VLMs for pseudo-labeling datasets with bounding box annotations.
CRIS-Team Scopus Importer:2025-04-11
Keywords: Large Language Model; Open Vocabulary Object Detection; Pseudo-Labeling; Vision Language Model

Optimization Problems in Production and Planning: Approaches and Limitations in View of Possible Quantum Superiority - Zwingel, Maximilian; May, Christopher; Reitelshöfer, Sebastian; Mauerer, Wolfgang (Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2023, 2025)
Modern production and process planning is characterized by complex and diffuse interrelationships of parameters, properties and control values. New materials, innovative production technologies, differing degrees of automatability and application dependency form a multidimensional problem space for optimization, which cannot be efficiently solved by today’s technologies. Approximations in form of genetic algorithms, different heuristics and simplifications exist, but lack applicability due to high runtime and estimation errors. Quantum computers, quantum annealers and hybrid algorithms show potential to offer added value and better performance over established approaches for optimization, planning and production control, but are often incomprehensible for production engineers. Based on an analysis of industrial problems in different domains and a definition of relevant problem cases, the potential of quantum systems for optimization in production and planning is explored. An approach to close the gap between classical and quantum optimization from an engineering standpoint is made by describing the transformation process for a real-world problem and discussing performance indicators of model implementation
Keywords: Production planning; Optimization; Quantum algorithms

Robotics simulation – A comparison of two state-of-the-art solutions - Zwingel, Maximilian; May, Christopher; Kalenberg, Matthias; Franke, Jörg (ASIM 2022 Proceedings Langbeiträge, 2022)
Simulation plays a significant role in development and deployment of robotics systems. Depending on the planned tasks, different functionalities shift into focus. For mobile robotics systems using the Robot Operating System (ROS), Gazebo has been the goto solution for simulating behavior, sensors, and interaction with environments for many years. With the availability of numerous novel solutions, a comparison of the different strengths from a use-case perspective becomes necessary. In our paper, we compare Gazebo and NVIDIAs Isaac Sim in the context of ROS-based autonomous mobile robots. Special attention is given to the integration of the simulators in the ROS-environment and their ability in regards to sensor simulation. This paper aims to provide a comparison between different features in Gazebo and NVIDIA Isaac Sim for simulating mobile robots and sensor systems. <br

Towards Imaginative Robots: A Generative Pipeline for Simulated Environments - May, Christopher; Suchy, Lorenz; Franke, Jörg; Reitelshöfer, Sebastian (Langbeiträge ASIM SST, 2024)
Autonomous Mobile Systems (AMS) offer significant advantages in industry and private sectors by adapting to diverse and dynamic environments. To train these systems, large amounts of data are required, typically obtained from simulated environments. However, the creation of these environments is often labor-intensive. Here, we propose a generative pipeline that provides a streamlined approach to virtual training and testing while allowing users to apply automated methods including generative AI. Our pipeline consists of four, partly iterative main steps. The pipeline spans from the creation of individual assets to the utilization of the simulated environments. The pipeline is then implemented for an exemplary scenario, utilizing multiple methods including generative AI. Furthermore, we propose a novel application of our pipeline to provide robots with the capabilities to “imagine” virtual experiences. The presented pipeline not only simplifies the process of generating simulated environments, but also resembles a scalable framework for developing increasingly complex AMS. <br