June 17-18, 2025 at Bielefeld University

Abstract:
Cognitive developmental robotics aims to develop robots capable of human-like learning, interaction, and behavior by grounding concrete and abstract concepts in sensorimotor experiences and social interactions. This talk introduced examples on language grounding in cognitive developmental robotics, and explores how principles like “starting small”, “embodied intelligence” and “super-embodiment” can address the limitations of AI tools, such as large language models (LLMs), which rely heavily on large datasets and lack sensorimotor grounding. By integrating incremental, multimodal learning and redefining embodiment to encompass physical, mental, and social processes, we can enable robots to better understand and utilize abstract concepts. The talk will also reflect on the pros and cons of using foundation models in cognitive robotics and consider research issues on explainable AI (XAI) and trust. 

About the Speaker:
Angelo Cangelosi is Professor of Machine Learning and Robotics at the University of Manchester (UK) and co-director and founder of the Manchester Centre for Robotics and AI. He was selected for the award of the European Research Council (ERC) Advanced grant (funded by UKRI). His research interests are in cognitive and developmental robotics, neural networks, language grounding, human robot-interaction and trust, and robot companions for health and social care. Overall, he has secured over £40m of research grants as coordinator/PI, including the ERC Advanced eTALK, the UKRI TAS Trust Node and CRADLE Prosperity, the US AFRL project THRIVE++, and numerous Horizon and MSCAs grants. Cangelosi has produced more than 400 scientific publications. He is Editor-in-Chief of the journals Interaction Studies and IET Cognitive Computation and Systems, and in 2015 was Editor-in-Chief of IEEE Transactions on Autonomous Development. He has chaired numerous international conferences, including ICANN2022 Bristol, and ICDL2021 Beijing. His book “Developmental Robotics: From Babies to Robots” (MIT Press) was published in January 2015, and translated in Chinese and Japanese. His latest book “Cognitive Robotics” (MIT Press), coedited with Minoru Asada, was recently published in 2022 (Chinese translation in 2025).

Abstract: 
The European Union’s approach to AI regulation focuses on transparency, accountability, and human oversight. Explainability is central to building responsible AI and influences both technical development and policy. This talk explores how explainability supports transparency, accountability, and human-centric values, all of which are key concerns in current EU debates on AI governance. Highlighting challenges and opportunities, I will outline how explainable AI can serve as a bridge between system design and societal expectations, ensuring that technological advancement is matched by ethical and legal responsibility.

About the Speaker:
Virginia Dignum is Professor of Responsible AI at Umeå University, Sweden, where she leads the AI Policy Lab. A Wallenberg Scholar and senior AI policy advisor, she chairs the ACM Technology Policy Council and is a Fellow of EURAI, ELLIS, and the Royal Swedish Academy of Engineering Sciences (IVA). She co-chairs the IEEE Global Initiative on AI Ethics and is an expert for UNESCO, OECD, and the Global Partnership on AI. She has advised the UN, EU, and WEF on AI governance and is a founder of ALLAI. Her upcoming book, The AI Paradox, is set for release in 2025.

Abstract: 
Insights from social sciences have transformed explainable artificial intelligence from a largely technical into a more human-centred discipline, thus enabling diverse stakeholders, rather than technical experts alone, to benefit from its developments. The focus of explainability research itself, nonetheless, remained largely unchanged, that is to help people understand the operation and output of predictive models. This, however, may not necessarily be the most consequential function of such systems; they can be adapted to complement, augment and enhance the abilities of humans instead of (fully) automating their various roles in an explainable way. In this talk I will explore how we can reimagine XAI by drawing upon a broad range of relevant interdisciplinary findings. The resulting, more comprehensive conceptualisation of the entire research field promises to be better aligned with humans by supporting their reasoning and decision-making in a data-driven way. As the talk will show, medical applications, as well as other high stakes domains, stand to greatly benefit from such a shift in perspective.

About the Speaker:
Kacper is a researcher in the Medical Data Science group at ETH Zurich. His main research focus is transparency – interpretability and explainability – of data-driven predictive systems based on artificial intelligence and machine learning algorithms intended for medical applications. Before, he was a Research Fellow at the ARC Centre of Excellence for Automated Decision-Making and Society, affiliated with the RMIT University in Melbourne, Australia. Prior to that he held numerous research positions at the University of Bristol, United Kingdom, working on multiple diverse AI and ML projects. Kacper holds a Master's degree in Mathematics and Computer Science and a doctorate in Computer Science from the University of Bristol.