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.
ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models
This paper introduces ExpliCa, a new dataset for evaluating LLMs in explicit causal reasoning.
Martina Miliani, Serena Auriemma, Alessandro Bondielli, Emmanuele Chersoni, Lucia Passaro, Irene Sucameli, and Alessandro Lenci. 2025. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17335–17355, Vienna, Austria. DOI: 10.18653/v1/2025.findings-acl.891
Fast: Similarity-Based Knowledge Transfer for Efficient Policy Learning
This work challenges the key issues in Transfer Learning to improve knowledge transfer, agents performance across tasks and reduce computational costs.
A. Capurso, E. Piccoli and D. Bacciu, 2025 IEEE Conference on Games (CoG), Lisbon, Portugal, 2025, pp. 1-8, doi: 10.1109/CoG64752.2025.11114355.
FinFix: A Soft Gripper With Contact-Reactive Reflex for High-Speed Pick and Place of Fragile Objects
This paper investigates using soft technology to solve the challenge of industrial automation calling for precise tasks with cycle times reduced to the minimum while maintaining accelerations low to keep interaction forces under a certain threshold to avoid damage when handling delicate products.
W. Heeringa, C. D. Santina and G. Smit, 2023 IEEE International Conference on Soft Robotics (RoboSoft), Singapore, Singapore, 2023, pp. 1-7, DOI: 10.1109/RoboSoft55895.2023.10122107.
Frappe: fast fiducial detection on low cost hardware
This paper introduces the Frappe (Fiducial Recognition Accelerated with Parallel Processing Elements) algorithm for detecting and decoding the popular ArUco tags.
Jones, S., & Hauert, S., Journal of Real-time Image Processing, 20, 119, 2023. DOI: 10.1007/s11554-023-01373-w
Gaussian Belief Propagation for Distributed Swarm Sensing
This paper presents how the Gaussian Belief Propagation (GBP) shows great potential as a general distributed knowledge inference algorithm for use within swarms of robots.
Simon Jones and Sabine Hauert, in ICRA2023 - Workshop on Distrbuted Graph Algorithms for Robotics.
Graph Adaptive Autoregressive Moving Average Models
Building on the connection between Autoregressive Moving Average (ARMA) and SSM, this paper introduces GRAMA, a Graph Adaptive method based on a learnable ARMA framework that addresses these limitations.
Moshe Eliasof, Alessio Gravina, Andrea Ceni, Claudio Gallicchio, Davide Bacciu, Carola-Bibiane Schönlieb, Proceedings of the 42 nd International Conference on Machine Learning, Vancouver, Canada. PMLR 267, 2025.
Graph Diffusion that can Insert and Delete
This paper reformulates the noising and denoising processes to support monotonic insertion and deletion of nodes.
Matteo Ninniri et al. 39th Conference on Neural Information Processing Systems (NeurIPS 2025).
Guiding Soft Robots with Motor-Imagery Brain Signals and Impedance Control
This work presents an alternative pathway towards safe and effective operation by combining wearable EEG with physically embodied safety in soft robots.
M. Stölzle, S. S. Baberwal, D. Rus, S. Coyle and C. D. Santina, in 2024 IEEE 7th International Conference on Soft Robotics (RoboSoft), San Diego, CA, USA, 2024, pp. 276-283, doi: 10.1109/RoboSoft60065.2024.10522005.
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
