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Christian Internó, Jumpei Yamaguchi, Markus Olhofer, David Klindt, Barbara Hammer , "Self-Supervised Learning Can Encode Physics, Finetuning Can Corrupt It", World Modeling Workshop (Mila - Quebec AI Institute, Montréal, Canada), 2026.
AbstractNeural simulators are increasingly used to model complex physical systems with high predictive accuracy. However, their internal "world models" are opaque, making it difficult to verify if they learn genuine physical principles or rely on brittle heuristics. In this work, we investigate whether mechanistic interpretability tools can reverse-engineer the physical laws from a model's internal representations. Our method involves attaching a linear ...
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Thomas H Weisswange , "Modelling Intended Impact of Assistive Interactions", Transparent and Interpretable Robots Conference (TRAIL 2026), 2026.
AbstractExplainability is first and foremost grounded in social interaction. While it is important to research transparent algorithms, understand causal attributions and design expressive interfaces when creating explainable agents, the target will always be to understand how to achieve a certain effect on a human perceiver. The need for an explanation only arises when part of the human's world model is flawed. For deciding when, what and how to communi...
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Radu Stoican, Christian Goerick, Angelo Cangelosi, Thomas H Weisswange , "Task-Specific Exploration in Meta-Reinforcement Learning via Task Reconstruction", Transactions on Machine Learning Research, 2026.
AbstractReinforcement learning trains policies specialized for a single task. Meta-reinforcement learning (meta-RL) improves upon this by leveraging prior experience to train policies for few-shot adaptation to new tasks. However, existing meta-RL approaches often struggle to explore and learn tasks effectively. We introduce a novel meta-RL algorithm for learning to learn task-specific, sample-efficient exploration policies. We achieve this through t...
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Daniel Tanneberg , "Embodied Intelligence: Artificial and Biological Neural Systems for Autonomous and Assistive Agents", ETH Zürich, 2026.
AbstractTalk at ETH Zürich, organized by our new collaboration partner Prof. Vörös, in combination with project Kickoff meeting. Topics will cover general HRI overview, published work on SMILE, previous personal work, and results from collaboration with CL from approved publication (pub-6649)....
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Beate Stattkus-Fortange, Christiane Attig, Christiane Wiebel, Thomas Franke , "Understanding the charging behavior of electric vehicle drivers on long-distance trips – The roles of range regulation and human-automation cooperation", Transportation Research Part F: Psychology and Behaviour, vol. 120, pp. 103610, 2026.
AbstractAdvances in battery technology and charging infrastructure have improved long-distance electric vehicle (EV) travel. However, effective trip planning can be challenging. EV trip planners (EVTs) can support drivers in range regulation, yet their effectiveness and acceptance depend on how drivers experience the interaction. In this context, we introduce Driver Electric Vehicle Trip Planner Interaction Style (DEVTIS), which captures individual diffe...
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Pietro Fanti, Leon Williams, Ondrej Dvorak, Marcus Märtens, Tat-Jun Chin, Hongbo Ji, Bofei Chen, Dongyu Xie, Kaifan Qiao, Bohao Li, Nils Einecke, Subramanian Arumugam, Amulya Ratna Padhy, Sumeet Kumar Rath, Swati Sonal Mahapatra, Dario Izzo , "Event-based lunar optical flow egomotion estimation challenge: design and results of the ELOPE competition", npj Space Exploration, 2026.
AbstractEvent-based vision is a promising technology with incredible potential for future space exploration. The Event-based Lunar OPtical flow Egomotion estimation (ELOPE) Challenge aims at evaluating and comparing approaches for lunar landing egomotion estimation using data from a single event-based camera. This work is based on the ELOPE Dataset, which is the first publicly available event-based camera dataset for lunar landing. Over 44 teams particip...
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Phillip Richter, Heiko Wersing, Anna-Lisa Vollmer , "A Taxonomy of Mental Model Mismatches for Human-Robot Interaction", Errors, Mistakes, and Failures in Humans and Robots (EMF) Workshop at HRI Conference, 2026.
AbstractMental model mismatches (MMM) in human-robot interaction occur when humans' internal representations of robots systematically diverge from robots' actual properties or behaviors, leading to communication breakdowns, task failures, and reduced trust. The field lacks shared terminology to identify specific types of mismatches. We present a theoretically grounded taxonomy of thirteen MMM types organized into three cognitive clusters. These are cogni...
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Jörg Deigmöller, Nakul Agarwal, Stephan Hasler, Daniel Tanneberg, Chao Wang, Reza Ghoddoosian, Felix Ocker, Anna Belardinelli, Fan Zhang, Behzad Dariush, Michael Gienger , "MERGE: Guided Vision-Language Models for Multi-Actor Event Reasoning and Grounding in Human–Robot Interaction", arXiv, 2026.
AbstractWe introduce MERGE, a system for situational grounding of actors, objects, and events in dynamic hu- man–robot group interactions. Effective collaboration in such settings requires consistent situational awareness, built on persistent representations of people and objects and an episodic abstraction of events. MERGE achieves this by uniquely iden- tifying physical instances of actors (humans or robots) and objects and structuring them into...
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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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