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Christiane Attig and Christiane Wiebel, "Beyond Tools: The Perception of AI as a Social Teammate in Human-AI Collaboration", 13. Fachgruppentagung der Fachgruppe Arbeits-, Organisations- und Wirtschaftspsychologie gemeinsam mit der Fachgruppe Ingenieurpsychologie (AOWI 2025), 2025.
AbstractHuman-AI interaction is often framed in terms of task efficiency, but users do not necessarily interact with AI systems as tools. Instead, they may perceive them as social partners with specified roles. The subjective ascription of roles in human-AI teaming can be understood as a multi-factorial, dynamic process, depending on task characteristics, AI functionality, and the user’s perception of the AI and the joint task. Consequently, to predic...
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Chao Wang, Jan Leusmann, Stephan Hasler, Anna Belardinelli, Sven Mayer, Michael Gienger, "Investigating LLM-Driven Curiosity in Human-Robot Interaction", CHI conference 2025, 2025.
AbstractIn the future, we need seamless and natural collaboration with robots. Currently, robots can only perform tasks that they have been taught either in the development stage or by using techniques like learning from demonstration. However, for humans, the natural way to learn is often through curiosity. Currently, it is unclear how users perceive the curiosity of robots. To address this, we developed a curious and a non-curious character using a Lar...
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Philipp Brockmann, Sebastian Brulin, Ilia Roisman, Jeanette Hussong, "Enhancement of interfacial instabilities by solid particles during fast stretching of a liquid suspension bridge", Soft Matter, pp. 16, 2025.
AbstractIn this experimental study, the rapid stretching dynamics and interfacial instabilities of a suspension liquid bridge are investigated using a high-speed video system. The bridge is formed between two parallel plates with one plate remaining fixed, while the other propagates with a constant acceleration reaching up to 160 m/s2. In these experiments, the initial gap width ranges from 30 to 60 µm. Sizes of the solid particles within the suspension...
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Faez Ahmed, Cyril Picard, Wei Chen, Christopher McComb, Pingfeng Wang, Ikjin Lee, Tino Stankovic, Douglas Allaire, Stefan Menzel, "Special Issue: Design by Data: Cultivating Datasets for Engineering Design", Journal of Mechanical Design, 2025.
AbstractThe transformative impact of data-driven methods, which have revolutionized fields like image and text analysis, relies on the availability of adequately large and diverse datasets. These datasets have fueled breakthroughs in deep learning, enabling the development of useful AI tools such as ChatGPT, Gemini, Llama, and Stable Diffusion. Similarly, in engineering design, data-driven methodologies are reshaping traditional paradigms—enhancing des...
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Felix Ocker, Stefan Menzel, Ahmed Sadik, Thiago de Jesus de Araujo Rios, "From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design ", arXiv, 2025.
AbstractCreating digital models using Computer Aided Design (CAD) is a process that requires in-depth expertise. In industrial product development, this process typically involves entire teams of engineers, spanning requirements engineering, CAD itself, and quality assurance. We present an approach that mirrors this team structure with a Vision Language Model (VLM)-based Multi Agent System, with access to parametric CAD tooling and tool documentation. Co...
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Sebastian Brulin, Tamon Toyooka, Lydia Fischer, Florian Kreuchauff, Tobias Rodemann, "Comparative Assessment of Swappable Battery Stations (SBEV) and Stationary Charging Stations (BEV) in Urban Electric Vehicle Networks: An Activity-Based Simulation Approach", EVTeC 2025, 2025.
AbstractThis paper investigates the comparative performance of swappable battery stations (SBEV) and stationary charging stations (BEV) in urban electric vehicle networks using large-scale activity-based MATSim simulations to assess user behavior across various scenarios. Focusing on user and operator perspectives, we investigate the hypotheses that SBEVs offer shorter charging times for users, while operators benefit from lower infrastructure costs eith...
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Elisabeth Menendez, Santiago Martinez, Carlos Balaguer, Michael Gienger, Anna Belardinelli, "SemanticScanpath: Combining Gaze and Speech for Situated Human-Robot Interaction Using LLMs", arxiv, 2025.
AbstractLarge Language Models (LLMs) have substan- tially improved the conversational capabilities of social robots. Nevertheless, for an intuitive and fluent human-robot inter- action, robots should be able to ground the conversation by relating ambiguous or underspecified spoken utterances to the current physical situation and to the intents expressed non verbally by the user, for example by using referential gaze. Here we propose a representati...
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Leon Keller, Daniel Tanneberg, Jan Peters, "Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning", IEEE International Conference on Robotics and Automation (ICRA), 2025.
AbstractImitation learning is a popular method for teaching robots new behaviors. However, most existing methods focus on teaching short, isolated skills rather than long, multi-step tasks. To bridge this gap, imitation learning algorithms must not only learn individual skills but also an abstract understanding of how to sequence these skill to perform extended tasks effectively. This paper addresses this challenge by proposing a neuro-symbolic imitatio...
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Simon Manschitz, Berk Güler, Wei Ma, Dirk Ruiken, "Sampling-Based Grasp and Collision Prediction for Assisted Teleoperation", International Conference on Robotics and Automation (ICRA), 2025.
AbstractShared autonomy allows for combining the global planning capabilities of a human operator with the strengths of a robot such as repeatability and accurate control. In a real-time teleoperation setting, one possibility for shared control is to let the human operator decide for the rough movement and to let the robot do fine adjustments, e.g., when the view of the operator is occluded. We present a learning-based concept for shared autonomy that ai...
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Berk Güler, Simon Manschitz, Kay Pompetzki, Jan Peters, "Towards Assistive Teleoperation for Knot Untangling", 1st German Robotics Conference, 2025.
AbstractManipulating deformable linear objects (DLOs) such as ropes is challenging due to their complex dynamics. To address these issues, we present a novel assistive teleoperation framework that combines human expertise with autonomous assistance. Our approach integrates a vision-based module to identify grasp poses, a shared autonomy mechanism that balances human input with autonomous guidance, and an optimization-based inverse kinematic solver for sm...
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