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.
Hedonic valence at the core of consciousness: A review of “A philosophy for the science of animal consciousness” by Walter Veit
A book review of “A philosophy for the science of animal consciousness” by Walter Veit Routledge.
Meertens, N. (2024). Philosophy and the Mind Sciences, 5. doi: 10.33735/phimisci.2024.11472
Heterogeneity of Faults in a Robot Swarm: Identifying Discriminatory Metrics
This paper presents an approach to faulty state discrimination through the lens of measuring diversity: can diversity be evaluated through discrimination of states of a system, and can we identify discriminatory metrics to apply to real-time diversity evaluation?
Suet Lee and Sabine Hauert, in ICRA 2023 - Heterogeneity in Multi-Robot Systems Workshop
Human cooperation with artificial agents varies across countries
This paper examined people’s willingness to cooperate with artificial agents and humans in two classic economic games requiring a choice between self interest and mutual benefit.
Karpus, J., Shirai, R., Verba, J.T. et al. Sci Rep 15, 10000 (2025). DOI: 10.1038/s41598-025-92977-8
I Know How: Combining Prior Policies to Solve New Tasks
This paper proposes a new framework, I Know How (IKH), which provides a common formalization. Our methodology focuses on modularity and compositionality of knowledge in order to achieve and enhance agent’s ability to learn and adapt efficiently to dynamic environments.
M. Li, E. Piccoli, V. Lomonaco and D. Bacciu, 2024 IEEE Conference on Games (CoG), Milan, Italy, 2024, pp. 1-4, doi: 10.1109/CoG60054.2024.10645586.
Improving Fairness via Intrinsic Plasticity in Echo State Networks
This paper addresses the problem of algorithmic fairness in Machine Learning for temporal data, focusing on ensuring that sensitive time-dependent information does not unfairly influence the outcome of a classifier.
Andrea Ceni, Davide Bacciu, Valerio De Caro, Claudio Gallicchio, and Luca Oneto, in ESANN 2023 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), doi: 10.14428/esann/2023.ES2023-90
In-Context Interference In Chat-Based Large Language Models
This paper focuses on interference in in-context learning, examining how new knowledge affects performance in self-aware robots.
Coleman, E.N., Hurtado, J., Lomonaco, V. (2024). European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. DOI: 10.1007/978-3-031-76424-0_21
Informed Machine Learning for Complex Data
This paper gathers valuable contributions and early findings in the field of Informed ML for Complex Data.
Luca Oneto, Nicolo Navarin, Alessio Micheli, Luca Pasa, Claudio Gallicchio, Davide Bacciu, Davide Anguita, in ESANN 2024 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 9-11 October 2024.
Input Decoupling of Lagrangian Systems via Coordinate Transformation: General Characterization and Its Application to Soft Robotics
Tthis article aims to answer the following question: Can a transformation of the generalized coordinates under which the actuators directly perform work on a subset of the configuration variables be found?
P. Pustina, C. D. Santina, F. Boyer, A. De Luca and F. Renda, in IEEE Transactions on Robotics, vol. 40, pp. 2098-2110, 2024, doi: 10.1109/TRO.2024.3370089.
Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
This paper argues that a promising avenue to the efficient and effective latent-space control of physical systems is to leverage powerful and well-understood closed-form strategies from control theory literature in combination with learned dynamics.
Maximilian Stölzle, Cosimo Della Santina. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pp. 82010-82059, 2024.
Intelligence brings responsibility - Even smart AI assistants are held responsible
This paper examines whether purely instrumental AI systems stay clear of responsibility by comparing AI-powered with non-AI-powered car warning systems and measured their responsibility rating alongside their human users.
Deroy, O., Longin, L., & Bahrami, B., Iscience, 26(8), 107494, 2023. DOI: 10.1016/j.isci. 2023.107494
Interacting with agents without a mind: the case for artificial agents
Do people attribute human traits to non-human entities without a mind, such as AI? This review shows how AI fails to fully elicit these two dimensions of mind perception.
Geiselmann, R., Tsourgianni, A., Deroy, O., & Harris, L. T. (2023). Opinion in Behavioral Sciences, 51, 101282.. DOI: 10.1016/j.cobeha.2023.101282
Investigating over-parameterized randomized graph networks
This paper investigates neural models based on graph random features for classification tasks.
Giovanni Donghi, Luca Pasa, Luca Oneto, Claudio Gallicchio, Alessio Micheli, Davide Anguita, Alessandro Sperduti, Nicolò Navarin, Neurocomputing, 606, 2024, 128281, doi: 10.1016/j.neucom.2024.128281.
Investigating Time-Scales in Deep Echo State Networks for Natural Language Processing
This paper analyses the performance and the dynamical behaviour of Reservoir Computing models, specifically Deep Bidirectional Echo State Networks, applied to Natural Language Processing tasks.
Baccheschi, C. et al. (2026). ICANN 2025 International Workshops and Special Sessions. ICANN 2025. Lecture Notes in Computer Science, vol 16072. DOI: 10.1007/978-3-032-04552-2_18
Learning 3D shape proprioception for continuum soft robots with multiple magnetic sensors
This letter proposes to use magnetic sensors fully integrated into the robot to achieve proprioception, and a neural architecture to make sense of the highly nonlinear relationship between the perceived intensity of the magnetic field and the shape of the robot.
T. Baaij et al., Soft Matter, vol. 19, no. 1, pp. 44–56, 2023, DOI: 10.1039/D2SM00914E.
Learning causal abstractions of linear structural causal models
This work tackles the issues of modelling causal knowledge for linear causal models with linear abstraction functions.
Riccardo Massidda, Sara Magliacane and Davide Bacciu, 2025, in Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, 118, 30.
Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control
This paper introduces a streamlined method for learning low-dimensional, physicsbased models that are both accurate and easy to interpret.
R. Valadas, M. Stölzle, J. Liu and C. D. Santina, 2025 IEEE 8th International Conference on Soft Robotics (RoboSoft), Lausanne, Switzerland, 2025, pp. 1-8.
Learning Multi-Reference Frame Skills from Demonstration with Task-Parameterized Gaussian Processes
This work proposes to learn such a representation without using task-specific heuristics within the context of multi-reference frame skill learning by superimposing local skills in the global frame.
M. R. Montero, G. Franzese, J. Kober and C. D. Santina, 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, 2024, pp. 2832-2839, doi: 10.1109/IROS58592.2024.10803060.
Lifelong Evolution of Swarms
This paper introduces a lifelong evolutionary framework for swarms, where a population of swarm controllers is evolved in a dynamic environment that incrementally presents novel tasks.
Lorenzo Leuzzi, Davide Bacciu, Sabine Hauert, Simon Jones, and Andrea Cossu. 2025. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '25). DOI: 10.1145/3712256.3726384
