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Michael Gienger , "Hey robot, pass me the apple: LLMs meet physical support", Talk at Virtual Robotics Lab International Lecture Series, Peru (https://roboticslab.pe), online, 2025.
AbstractThe recent breakthroughs in Generative AI offer fantastic opportunities to research novel concepts for intelligent embodied agents. In this talk, I will introduce recent research in exploiting Large Language Models (LLMs) for robot task and motion planning. We combined reasoning, planning, and motion generation, and introduced a novel concept for correcting errors during planning and execution. I’ll show several results both in simulations and re...
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Angie Nataly Melo Castillo, Markus Amann, Carlota Salinas Maldonado, Maytheewat Aramrattana, Thomas H Weisswange, Malte Probst , Miguel Ángel Sotelo , "Towards Incorporating Pedestrian Intention Predictions into Behavior Planning using Virtual Reality Co-Simulators", 36th IEEE Intelligent Vehicles Symposium (IV 2025): 14th Workshop on Human Factors in Intelligent Vehicles & Supporting Vehicle-Pedestrian Interactions, 2025.
AbstractInteraction modeling plays a huge role in understanding human behavior in traffic. This is especially relevant when it comes to interactions between vehicles and vulnerable road users such as pedestrians. Thus, pedestrian behavior prediction is an ongoing field of research in order to understand the pedestrians’ decision making. Most state-of-the-art prediction frameworks are trained on large-scale datasets and evaluated with respect to acknowled...
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Matti Krüger, Daniel Tanneberg, Chao Wang, Stephan Hasler, Michael Gienger , "Mirror Eyes: Explainable Human-Robot Interaction at a Glance", cs.RO, Cornell University, 2025.
AbstractThe gaze of a person tends to reflect their interest. This work explores what happens when this statement is taken literally and applied to robots. Here we present a robot system that employs a moving robot head with a screen-based eye model that can direct the robot's gaze to points in physical space and present a reflection-like mirror image of the attended region on top of each eye. We conducted a user study with 33 participants who were asked...
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William Huang, Yi Mei, Guenther Raidl, Fangfang Zhang, Laurenz Tomandl, Steffen Limmer, Mengjie Zhang, Tobias Rodemann , "Genetic Programming Hyper-Heuristic for Dynamic Electric Dial-a-Ride Problem", 2025 IEEE Congress on Evolutionary Computation (CEC), 2025.
AbstractThis paper studies the Dynamic Electric Dial-A-Ride Problem (DEDARP), which is a recent challenging combinatorial optimisation that has many applications in the real-world ridesharing services with electric vehicles. In addition to the challenges from the NP-hardness of dial-a-ride, we fact extra challenges for making real-time dispatching decisions in dynamic environments with unpredicted new requests and selecting proper time for the vehicles t...
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Svenja Kenneweg, Julian Eggert, Jörg Deigmöller, Philipp Cimiano , "A compositional approach to modeling the semantics of vague temporal adverbials", CogSci 2025, 2025.
AbstractVague temporal adverbials, such as "recently," "just," "some time ago," and "long time ago," describe the temporal distance between a past event and the utterance time but leave the exact duration under-specified. These adverbials' interpretation is influenced by the event's properties, like its duration and frequency. The paper introduces a compositional, cognitive model to represent these adverbials' semantics as probabilistic distributions, co...
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Felix Ocker , "When robots take initiative: Agentic AI for human-robot cooperation", summit munich_i, 2025.
AbstractAs robots move beyond rigid, rule-based behavior, a new class of intelligent agents is emerging—robots that take initiative, act with purpose, and collaborate with humans. This talk explores the shift toward agentic AI, highlighting key design patterns and recent breakthroughs that have the potential to make autonomy not just possible, but useful. From language model-based robots that offer support only when truly needed, to systems that plan for...
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Andrea Castellani , "Real-World Energy Management Data from a Smart Building for Optimization and Machine Learning", Deep Learning Techniques for Observable Smart Grid and Sustainable Energy Systems (Workshop at IJCNN 2025), 2025.
AbstractThis tutorial presents a real-world energy management dataset collected from a smart company building over six years’ time. The dataset includes energy consumption from various sites, renewable energy production from photovoltaic and combined heat-and-power systems, and detailed heating and cooling system operations. It also contains high-resolution weather station measurements, making it a valuable resource for energy analysis and optimization. ...
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Qingshan Xu, Jiao Liu, Melvin Wong, Caishun Chen, Yew Soon Ong , "Looks Great, Functions Better: Physics Conform Text-to-3D Shape Generation", International Joint Conference on Neural Networks, 2025.
AbstractText-to-3D shape generation has shown great promise in generating novel 3D content based on given text prompts. However, existing generative methods mainly consider geometric or visual plausibility while ignoring functionality for the generated 3D shapes. This greatly hinders the practicality of generated 3D shapes in real-world applications. Towards physical AI, we propose Fun3D, a physics conform functional text-to-3D shape generation method. B...
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Phillip Richter, Heiko Wersing, Anna-Lisa Vollmer , "Improving Human-Robot Teaching by Quantifying and Reducing Mental Model Mismatch", arXive, 2025.
AbstractThe rapid development of artificial intelligence and robotics has had a significant impact on our lives, with intelligent systems increasingly performing tasks traditionally performed by humans. Efficient knowledge transfer requires matching the mental model of the human teacher with the capabilities of the robot learner. This paper introduces the Mental Model Mismatch (MMM) Score, a feedback mechanism designed to quantify and reduce misma...
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Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao , "Learning to Expand/Contract Pareto Sets in Dynamic Multiobjective Optimization With a Changing Number of Objectives", IEEE Transactions on Evolutionary Computation, 2025.
AbstractDynamic multiobjective optimization problems (DMOPs) with a changing number of objectives (NObjs) may have Pareto-optimal set (PS) manifold expanding or contracting over time. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one problem instance to solve another related problem instance. However, we show that the state-of-the-art transfer approach based on heuristic lacks diversity on probl...
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