EMERGE’s findings will be communicated at scientific conferences and published in open-access journals. Find below the current list of publications.
Access all publication on the project's Zenodo community clicking here.
3D Printable Gradient Lattice Design for Multi-Stiffness Robotic Fingers
This paper focuses on the development of a robotic finger that emulates these multi-stiffness characteristics.
S. J. Schouten et al., 2025 IEEE 8th International Conference on Soft Robotics (RoboSoft), Lausanne, Switzerland, 2025, pp. 1-7, doi: 10.1109/RoboSoft63089.2025.11020868.
A Data-Driven Method to Identify Fault Mitigation Strategies in Robot Swarms
In this paper, the authors present a data-driven method to identify effective local actions available to faulty robots in the swarm.
Lee, S., Hauert, S. (2024). Swarm Intelligence. ANTS 2024. Lecture Notes in Computer Science, vol 14987. DOI: 10.1007/978-3-031-70932-6_2
A Framework for the Examination of Awareness in Artificial Systems
This paper proposes a novel and tractable approach to measure the impact of awareness on system performance, structured around distinct dimensions of awareness – temporal, spatial, metacognitive, self and agentive.
Lee, S., Meertens, N., Milner, E., Hauert, S. (2026). In Biomimetic and Biohybrid Systems. Living Machines 2025. Lecture Notes in Computer Science, vol 15582. DOI: 10.1007/978-3-032-07448-5_27
A Hybrid Control Approach for a Pneumatic-Actuated Soft Robot
This paper proposes a hybrid controller for a pneumatic-actuated soft robot.
Tavio y Cabrera, E., Santina, C.D., Borja, P. (2024). In Proceedings in Advanced Robotics, vol 29. doi: 10.1007/978-3-031-55000-3_2
A memristive computational neural network model for time-series processing
In this work, the authors introduce a novel computational framework inspired by the physics of memristive devices and systems, which is embed into the context of Recurrent Neural Networks (RNNs) for time-series processing.
Veronica Pistolesi, Andrea Ceni, Gianluca Milano, Carlo Ricciardi, Claudio Gallicchio; APL Mach. Learn. 1 March 2025; 3 (1): 016117. DOI: 10.1063/5.0255168
A Model of Memristive Nanowire Neuron for Recurrent Neural Networks
This work proposes a novel neural processing unit for artificial neural networks, inspired by the memristive properties of nanowires.
Pistolesi, Veronica & Ceni, Andrea & Milano, Gianluca & Gallicchio, Claudio. (2025). 479-484. 10.14428/esann/2025.ES2025-104.
A Protocol for Continual Explanation of SHAP
In this work, the authors study the behavior of SHAP values explanations in Continual Learning and propose an evaluation protocol to robustly assess the change of explanations in Class-Incremental scenarios.
Andrea Cossu, Francesco Spinnato, Riccardo Guidotti and Davide Bacciu, in ESANN 2023 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), doi: 10.14428/esann/2023.ES2023-41
A Provably Stable Iterative Learning Controller for Continuum Soft Robots
This letter proposes a purely feedforward iterative learning control algorithm that refines the torque action by leveraging both the knowledge of the model and data obtained from past experience.
M. Pierallini et al., IEEE Robotics and Automation Letters, vol. 8, no. 10, pp. 6427-6434, 2023, DOI: 10.1109/LRA.2023.3307007.
Adaptive LoRA Merging for Efficient Domain Incremental Learning
This paper addresses a key limitation of current merging algorithms: their overreliance on fixed weights that usually assume equal importance across tasks.
Eric Nuertey Coleman, Luigi Quarantiello, Julio Hurtado, Vincenzo Lomonaco, NeuroIPS 2024, Adaptive Foundation Models: Evolving AI for Personalized and Efficient Learning, 2024.
AI’s assigned gender affects human-AI cooperation
This study investigates how cooperation varies with the gender labels assigned to AI partners.
Bazazi, Sepideh et al., "AI’s assigned gender affects human-AI cooperation", iScience, Volume 28, Issue 12, 113905. DOI: 10.1016/j.isci.2025.113905
All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark
This paper introduces MAIA (Multimodal AI Assessment), a native-Italian benchmark designed for fine-grained investigation of the reasoning abilities of visual language models on videos.
Davide Testa, Giovanni Bonetta, Raffaella Bernardi, Alessandro Bondielli, Alessandro Lenci, Alessio Miaschi, Lucia Passaro, and Bernardo Magnini. 2025. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 20030–20050, Suzhou, China.
An Empirical Investigation on Variational Autoencoder-Based Dynamic Modeling of Deformable Objects from RGB Data
This paper explores the use of deep learning techniques to solve the nonlinear identification problem of the dynamics of continuously deformable objects and other mechanical systems analytically from first principles.
T. Coleman, R. Babuška, J. Kober and C. D. Santina, 2024 32nd Mediterranean Conference on Control and Automation (MED), Chania - Crete, Greece, 2024, pp. 921-928, doi: 10.1109/MED61351.2024.10566173.
An Experimental Study of Model-Based Control for Planar Handed Shearing Auxetics Robots
This paper presents a model-based control strategy for planar HSA robots enabling regulation in task space.
Stölzle, M., Rus, D., Della Santina, C. (2024). In Experimental Robotics. ISER 2023. Springer Proceedings in Advanced Robotics, vol 30. doi:10.1007/978-3-031-63596-0_14
An Untrained Neural Model for Fast and Accurate Graph Classification
This paper aims to explore a simple form of a randomized graph neural network inspired by the success of randomized convolutions in the 1-dimensional domain.
Navarin, N., Pasa, L., Gallicchio, C., Sperduti, A. (2023). In Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. doi: 10.1007/978-3-031-44216-2_23
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
In this work, the authors present Anti-Symmetric Deep Graph Networks (A-DGNs), a framework for stable and non-dissipative DGN design, conceived through the lens of ordinary differential equations.
Gravina, A., Bacciu, D., & Gallicchio, C. (2022). Proceedings of the 11th International Conference on Learning Representations (ICLR). doi: 10.48550/arXiv.2210.09789.
Automating the Evaluation of the Scalability, Flexibility, and Robustness of Collective Behaviors for Robot Swarms
In this paper, the authors use recently proposed experimental protocols to evaluate these properties in various typical collective behaviors for robot swarms.
G. M. Madroñero Pachajoa, W. Achicanoy and D. G. Ramos, 2024 Brazilian Symposium on Robotics (SBR) and 2024 Workshop on Robotics in Education (WRE), Goiania, Brazil, 2024, pp. 144-149, doi: 10.1109/SBR/WRE63066.2024.10837963.
Awareness in Robotics: An Early Perspective from the Viewpoint of the EIC Pathfinder Challenge “Awareness Inside”
This paper summarizes and discusses the projects funded by the EIC Pathfinder Challenge “Awareness Inside” call within Horizon Europe, designed specifically for fostering research on natural and synthetic awareness.
Della Santina, C. et al. (2024). In European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. doi: 10.1007/978-3-031-76424-0_20
Back to Bee-sics: Learning Information Sharing Strategies for Robot Swarms Through the Hive
This study uses a learning-based approach to optimise information sharing in a hybrid robot swarm, where each robot maintains local autonomy, but information is shared via a central repository.
Henry Hickson, Sabine Hauert, Alex Mavromatis (2025). Proceedings of the ALIFE 2025: Ciphers of Life: Proceedings of the Artificial Life Conference 2025. Kyoto, Japan. (pp. 78). DOI: 10.1162/ISAL.a.906
