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 practical guide to streaming continual learning
This work discusses Streaming Continual Learning (SCL), an emerging paradigm providing a unifying solution to real-world problems, which may require both SML and CL abilities.
Andrea Cossu, Federico Giannini, Giacomo Ziffer, Alessio Bernardo, Alexander Gepperth, Emanuele Della Valle, Barbara Hammer, Davide Bacciu, Neurocomputing, 674, 2026. DOI: 10.1016/j.neucom.2026.132951
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.
A Scalable, Open and Remote Laboratory Architecture for Swarm Robotics Experimentation and Education
This work presents ATRIZ: a remote and open laboratory for swarm robotics.
López, B., Achicanoy, W., Ramos, D.G. (2026). Advances in Automation and Robotics Research. LACAR 2025. Lecture Notes in Networks and Systems, vol 1853. doi: 10.1007/978-3-032-18982-0_15
Adapting language models with continual learning for temporal drifts
This work addresses the challenge of keeping LLMs up to date with factual knowledge (adaptation) while avoiding forgetting the relevant existing knowledge.
Carta, A., Marinelli, A.R. & Passaro, L.C. Evolving Systems 17, 53 (2026). DOI: 10.1007/s12530-026-09817-x
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.
Adaptive Memory Retention in Dynamic Graphs
This work introduces LAMP, a dynamic graph model for snapshot-based dynamic graphs that incorporates adaptive, learned dissipation within a principled dynamical systems framework.
Fabrizio De Castelli, Alessio Gravina, Moshe Eliasof, Carola-Bibiane Schönlieb, Davide Bacciu, in Proc. Forty-third International Conference on Machine Learning, 2026.
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 Comparison of the Most Popular Approaches to Fake News Detection
This paper presents a critical assessment of 12 representative fake news detection approaches, spanning traditional machine learning, deep learning, transformers, and specialized cross-domain architectures.
Pietro Dell’Oglio, Alessandro Bondielli, Francesco Marcelloni, Lucia C. Passaro, Information Sciences, v. 745, 2026. DOI: 10.1016/j.ins.2026.123407.
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
