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Fan Zhang and Michael Gienger , "Learning Robot Manipulation from Audio World Models", IEEE ICRA 2026 Workshop Manipulation Multimodal Embodied Interaction in Robots Learning, 2026.
AbstractWorld models have demonstrated impressive performance on robotic learning tasks. Many such tasks inherently demand multimodal reasoning; for example, filling a bottle with water can make visual information alone ambiguous or incomplete, thereby requiring reasoning about the temporal evolution of audio, accounting for its underlying physical properties and pitch patterns. In this paper, we propose a generative latent flow matching model to anticip...
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Christiane Wiebel , "Successful Human-AI Cooperation from a Psychological Perspective", AI Talks Universitaet Wuerzburg, 2026.
AbstractCooperation is omnipresent in nature, from simple organisms that live in symbiosis to the creation of complex human societies. Recent research in AI-driven technology has claimed that AI systems need to learn to engage in cooperative interactions with humans to be successfully adopted in the future (Dafoe, 2020, 2021). However, what counts as “successful” human-AI cooperation from the human perspective remains an open question. In this talk, I w...
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Stefan Menzel, Yew Soon Ong, Yaochu Jin, Niki van Stein, Anna Kononova, Thomas Bäck, Bernhard Sendhoff , "Special Session on Generative AI and Heuristic Optimization", IEEE Congress on Evolutionary Computation, 2026.
AbstractGenerative AI and Large Language Models are groundbreaking technological innovations with large impact on contemporary science and engineering by generating context-sensitive text, knowledge-based answers, software code, images, music, and 3D assets from text prompts and image inputs. Trained on large datasets, these models conserve knowledge, identify hidden patterns, and reason across diverse data modalities. In evolutionary computation, genera...
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Tim Puphal , "A Survey of Robust Motion Planning: Interaction Modeling, Uncertainty Handling and Learning-Based Robustness ", IEEE International Conference on Intelligent Transportation Systems (ITSC), 2026.
AbstractAchieving robust autonomous driving remains a major challenge in translating research systems into widespread real-world deployment. Unlike scalable software technologies, autonomous vehicles must perform reliably across a wide range of conditions, including rare, safety-critical driving situations. This survey reviews research aimed at increasing the robustness of autonomous driving, with a particular focus on motion planning approaches that exp...
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Johnson Zhou, Daniel Tanneberg, Forough Habibollahi, Alon Loeffler, Kiaran Lawson, Valentina Baccetti, Kwaku Dad Abu-Bonsrah, Candice Desouza, Finn Doensen, Bradley Watmuff, Daria Kornienko, Azin Azadi, Justin L. Bourke, Bernhard Sendhoff, Brett J. Kagan , "Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation", arXiv, 2026.
AbstractBiological neural networks (BNNs) have been established as a powerful and adaptive substrate that offer the potential for incredibly energy and data efficient information processing with distinct learning mechanisms. Yet a core challenge to utilizing BNN for neurocomputation is determining the optimal encoding and decoding mechanisms between the traditional silicon computing interface and the living biology. Here, we propose an Embodied Neurocomp...
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Fan Zhang and Michael Gienger , "Affordance-based Robot Manipulation with Flow Matching", ICRA 2026 Workshop on Semantics for Reliable Robot Autonomy: From Environment Understanding and Reasoning to Safe Interaction, 2026.
AbstractWe present a framework for assistive robot manipulation that addresses two fundamental challenges: efficient adaptation of large-scale models for scene affordance understanding and effective learning of robot actions by grounding the visual affordance. To tackle the first challenge, we adopt a parameter-efficient prompt tuning method, prepending learnable text prompts to a frozen vision model to predict affordances, while considering spatial and ...
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Chao Wang, Fan Zhang, Michael Gienger , "Emotion Retargeting for Social Physical Human–Robot Interaction: Real-Time Learning of Expressive Robot Behaviors via Full-Body Human Mapping", IEEE ICRA 2026 Workshop Learning-HRI, 2026.
AbstractExpressive behavior is essential for robots to effectively convey emotional states during interactions with humans, particularly in social physical human–robot interaction (spHRI) scenarios. We present a framework for collecting realistic and diverse robotic emotional expressions through expert demonstrations captured using a mixed reality (MR) headset. Our system enables experts to teleoperate both virtual and physical robots from a first-person...
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Muhammad Ashfaq, Ahmed Sadik, Tommi Mikkonen, Muhammad Waseem, Niko Mäkitalo , "Runtime composition in dynamic system of systems: A systematic review of challenges, solutions, tools, and evaluation methods", Journal of Systems and Software, vol. 232, no. 112661, 2026.
AbstractSystem of Systems (SoS) represents a collection of constituent systems (CS) working together to achieve objectives unattainable by individual systems. With increasing relevance in domains such as smart cities, transportation, and robotics, the need for adaptable and dynamic SoS has emerged. Unlike traditional SoS architectures like UAF, DoDAF, and MODAF, which rely on static and manual integration, modern SoS demand dynamic, runtime assembly to m...
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Jouh Yeong Chew, Alan Sarkisian, Christiane Wiebel, Christiane Attig, Zhaobo Zheng , "Workshop on Socially Aware and Cooperative Intelligent Systems", HAI '25: Proceedings of the 13th International Conference on Human-Agent Interaction, pp. 580-582, 2026.
AbstractIn an increasingly interconnected world, the role of intelligent systems is rapidly evolving beyond isolated decision-making and task execution. AI agents and systems are now expected not only to perform efficiently but also to engage responsibly within complex social environments. This workshop theme centers on the development of AI agents and systems that are capable of understanding, adapting to, and reacting to collaborate with humans in mea...
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Matti Krüger, Yutaka Ooshima, Yu Fang , "Virtual Reflections on a Dynamic 2D Eye Model Improve Spatial Reference Identification", IEEE Transactions on Human-Machine Systems, 2026.
AbstractThe visible orientation of human eyes creates some transparency about people's spatial attention and other mental states. This leads to a dual role of the eyes as a means of sensing and communication. Accordingly, artificial eye models are being explored as communication media in human-machine interaction scenarios. One challenge in the use of eye models for communication consists of resolving spatial reference ambiguities, especially for screen...
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