Enrico Donato

PostDoctoral Researcher

BRAIR Lab @ The BioRobotics Institute, Sant'Anna School of Advanced Studies, Italy

Magdalena Yordanova

PhD Student

Institut für Informatik @ Humboldt-Universität zu Berlin, Germany

Verena V. Hafner

Full Professor

Institut für Informatik @ Humboldt-Universität zu Berlin, Germany

Vincenzo Lomonaco

Associate Professor

Department of AI, Data and Decision Sciences, LUISS Guido Carlo University, Italy

Egidio Falotico

Associate Professor

BRAIR Lab @ The BioRobotics Institute, Sant'Anna School of Advanced Studies, Italy

Vincenzo Lomonaco Associate Professor 

COLLAGE Lab Department of AI, Data and Decision Sciences, LUISS Guido Carlo University, Italy

Talk: Continual model routing for robotics

Abstract. AI model hubs now host thousands of pre-trained models, enabling powerful mixture-of-experts systems—but selecting and continually adapting the right models as tasks and repositories evolve is a major challenge. In this talk, I introduce Continual Model Routing (CMR), a framework for lifelong model selection, and CMRBench, a large-scale benchmark simulating realistic hub growth. I will present CARvE, a contrastive embedding approach that uses domain-stratified coreset replay and checkpoint-based anchoring to efficiently route models. Empirical results show CARvE outperforms zero-shot retrieval and fine-tuning baselines, highlighting the promise of scalable, adaptive model routing for robotics and open-world AI systems.

Short Bio. Vincenzo is Associate Professor in the Department of AI, Data and Decision Sciences at Luiss Guido Carli University, where he leads the Collage Lab. He is also the co-founder of ContinualIST, a spin-off of the University of Pisa, a founding member of the non-profit organization ContinualAI, and the Principal Investigator of several research projects and industrial collaborations. In just six years of research following his PhD, he has secured over €2 million in funding, including the prestigious Italian Science Fund (FIS) – Starting Grant, a PRIN, and collaborations with international organizations such as Intel, Meta, Leonardo, and ESA. Over the past decade, he has published more than 80 scientific papers in top-tier conferences and journals on the topic of Sustainable Artificial Intelligence. His pioneering research in continual and decentralized machine learning has been recognized with the prestigious Marco Somalvico Award 2025 from the Italian Association for Artificial Intelligence.

Verena V. Hafner Full Professor

Institut für Informatik Humboldt-Universitat zu Berlin, Germany

Talk: Continual Learning for a Robotic Self

Abstract. TBC

Short Bio. Verena is Full Professor at Humboldt-Universitat zu Berlin, she has led numerous EU projects on developmental robotics. Her research explores lifelong learning and autonomous knowledge structuring through physical and social interaction.

Sylvain Calinon Senior Research Scientist

Idiap Research Institute & EPFL, Switzerland

Talk: Frugal Learning for Physical Human-Robot Collaboration

Abstract.  Despite significant advances in AI, robots still struggle with tasks involving physical interaction. Robots can easily beat humans at board games such as Chess or Go but struggle to skillfully move the game pieces by themselves (the part of the task that humans subconsciously succeed in). Learning manipulation skills is both hard and fascinating because the movements and behaviors to acquire are tightly connected to our physical world and to embodied forms of intelligence.

I will present an overview of representations and learning approaches that can help robots acquire manipulation skills through physical human-robot collaboration. I will present the advantages of targeting a frugal learning approach, where the term "frugality" has two goals: 1) learning manipulation skills from only few demonstrations or exploration trials; and 2) learning only the components of the skill that really need to be learned.

Toward this goal, I will emphasize the roles of geometry, manifolds, implicit shape representations and distance fields as inductive biases to facilitate human-guided manipulation skill acquisition. I will also show how ergodic control can provide a mathematical framework to generate exploration and coverage movement behaviors, which can be exploited by robots as a way to cope with uncertainty in sensing, proprioception and motor control. 

Short Bio. Sylvain is a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He heads the Robot Learning & Interaction group at Idiap, with expertise in human-robot collaboration, robot learning from demonstration, geometric representations and optimal control. The approaches developed in his group can be applied to a wide range of applications requiring manipulation skills, with robots that are either close to us (assistive and industrial robots), parts of us (prosthetics and exoskeletons), or far away from us (shared control and teleoperation). 

Lorenzo Natale Senior Researcher

Humanoid Sensing and Perception lab Italian Institute of Technology, Italy

Talk: Towards embodied intelligence in humanoid robots

Abstract. This talk explores embodied intelligence as a route toward humanoid robots that can learn and adapt through interaction with the physical world. While current AI systems have achieved remarkable progress, their application to robotics is still hampered by the lack of suitable training data and effective online adaptation methods. I will present recent work that aims to go beyond these limitations by taking advantage of cross-embodiment learning, active learning, and multimodal integration. Together, these examples show how robot embodiment can be leveraged to support more adaptive humanoid behavior.

Short Bio. Lorenzo is Principal Investigator and Senior Researcher at the Italian Institute of Technology (IIT), where he leads the Humanoid Sensing and Perception group. He develops perception, sensing, and manipulation technologies for humanoid robots, contributing to platforms such as iCub and R1. His research focuses on robot vision, tactile sensing, active perception, and software architectures that enable intelligent and adaptive robot behavior.

Egidio Falotico Associate Professor

BRAIR Lab The BioRobotics Institute, Scuola Superiore Sant'Anna, Italy

Talk: Brain-Inspired Robot Learning: Adaptation, Memory and Interaction

Abstract. TBC

Short Bio. Egidio is Associate Professor at Scuola Superiore Sant’Anna, where he specializes in brain-inspired robot motor control and sensorimotor coordination. He focuses on integrating cognitive models with (soft) robots to test coherence, persistence, and adaptability of learned behaviours.

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