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.
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.
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. Our idea is pretty simple: implement a no-frills convolutional graph neural network and leave its weights untrained.
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). arXiv preprint. doi: 10.48550/arXiv.2210.09789.
Building Trustworthiness by Minimizing the Sim-to-Real Gap in Fault Detection for Robot Swarms
In this work, the authors implement metric extraction in a real-world environment and evaluate whether the extracted metrics can overcome the “sim-to-real gap”
Suet Lee and Sabine Hauert. 2023. In Proceedings of the First International Symposium on Trustworthy Autonomous Systems (TAS '23). Association for Computing Machinery, New York, NY, USA, Article 47, 1–3. doi: 10.1145/3597512.3597527
Diversifying Non-dissipative Reservoir Computing Dynamics
In this paper, the authors propose alternative formulations of the reservoirs for EuSNs, aiming at improving the diversity of the resulting dynamics.
Gallicchio, C. (2023). In Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. doi: 10.1007/978-3-031-44198-1_15
Evolving and generalising morphologies for locomoting micro-scale robotic agents
This paper explores how the morphology of a multi-cellular micro-robotic agent can be optimised for reliable locomotion using artificial evolution in a stochastic environment.
Uppington, M., Gobbo, P., Hauert, S., & Hauser, H. Journal of Micro and Bio Robotics, 1-11, 2023. DOI: 10.1007/s12213-023-00155-8
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
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
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? Perceived humanness is based on the assumption that the other can act (has agency) and has experiences (thoughts and feelings). This review shows how AI fails to fully elicit these two dimensions of mind perception.
Deroy, O., Current Opinion in Behavioral Sciences, 51, 101282, 2023. DOI: 10.1016/j.cobeha.2023.101282
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.
Model-Based Control for Soft Robots With System Uncertainties and Input Saturation
This article aims at solving challenges regarding accuracy and actuation by proposing a robust model-based strategy for the shape control of soft robots with system uncertainty and input saturation.
Shao, X., Pustina, P., Stölzle, M., Sun, G., De Luca, A., Wu, L., & Della Santina, C., IEEE Transactions on Industrial Electronics, 1–10, 2023. DOI: 10.1109/TIE.2023.3303636
Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges
In continuum soft robotics, softness is not concentrated at the joint level but instead distributed across the whole structure. As a result, soft robots (henceforth, omitting the adjective continuum) are entirely made of continuously deformable elements.
C. Della Santina, C. Duriez and D. Rus, IEEE Control Systems Magazine, vol. 43, no. 3, pp. 30-65, 2023, DOI: 10.1109/MCS.2023.3253419.
Modelling Handed Shearing Auxetics: Selective Piecewise Constant Strain Kinematics and Dynamic Simulation
This paper proposes two key components extending discrete Cosserat rod model (DCM) to allow for modeling Handed Shearing Auxetics (HSAs) for electrically-actuated continuum soft robots.
M. Stölzle, L. Chin, R. L. Truby, D. Rus and C. D. Santina, 2023 IEEE International Conference on Soft Robotics (RoboSoft), 2023, pp. 1-8, DOI: 10.1109/RoboSoft55895.2023.10121989.
Modular Wavelength Adaptation of the Dynamic Optical MicroEnvironment
In this paper, the authors present a modular solution to allow general light sources to be used with the DOME. By switching to a high-powered near-UV light source, we show that DNA damage can be caused by the Epi-DOME's projection system at a targeted location.
N. Wijewardhane, M. Uppington, M. How, H. Hauser, E. Piddini and S. Hauert, 2023 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), Abu Dhabi, United Arab Emirates, 2023, pp. 1-6, doi: 10.1109/MARSS58567.2023.10294114.
Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems
This letter concerns control-oriented and structure-preserving learning of low-dimensional approximations of high-dimensional physical systems, with a focus on mechanical systems.
M. Lepri, D. Bacciu and C. D. Santina, "Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems," in IEEE Control Systems Letters, vol. 8, pp. 133-138, 2024, DOI: 10.1109/LCSYS.2023.3344286.
Prescribing Cartesian Stiffness of Soft Robots by Co-Optimization of Shape and Segment-Level Stiffness
In this work, the authors propose a strategy to prescribe variations of the physical stiffness and the robot's posture so to implement a desired Cartesian stiffness and location of the contact point.
Francesco Stella, Josie Hughes, Daniela Rus, and Cosimo Della Santina. Soft Robotics. Aug 2023. 701-712. DOI: 10.1089/soro.2022.0025.
Proprioceptive Sensing of Soft Tentacles with Model Based Reconstruction for Controller Optimization
In this work, the authors propose a new sensing approach for soft underwater slender structures based on embedded pressure sensors and use a learning-based pipeline to link the sensor readings to the shape of the soft structure.
A. Vicari et al., 2023 IEEE International Conference on Soft Robotics (RoboSoft), Singapore, Singapore, 2023, pp. 1-6, doi: 10.1109/RoboSoft55895.2023.10121999.
Residual Reservoir Computing Neural Networks for Time-series Classification
In this paper, the authors augment standard Echo State Networks (ESNs) with linear reservoir-skip connections modulated by an untrained orthogonal weight matrix.
Andrea Ceni and Claudio Gallicchio, in ESANN 2023 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), doi: 10.14428/esann/2023.ES2023-112
Smell Driven Navigation for Soft Robotic Arms: Artificial Nose and Control
This work proposes an artificial nose on a soft robotic arm that ensures separate smell concentration readings.
F. Piqué, F. Stella, J. Hughes, E. Falotico and C. D. Santina, 2023 IEEE International Conference on Soft Robotics (RoboSoft), Singapore, Singapore, 2023, pp. 1-7, doi: 10.1109/RoboSoft55895.2023.10122116.
Soft Robot Shape Estimation With IMUs Leveraging PCC Kinematics for Drift Filtering
This letter proposes a method to eliminate this limitation by leveraging the Piecewise Constant Curvature model assumption.
F. Stella, C. D. Santina and J. Hughes, in IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1945-1952, Feb. 2024, doi: 10.1109/LRA.2023.3339063.
The Ethics of Terminology: Can We Use Human Terms to Describe AI?
The article challenges the justifications for the linguistic practices of for assigning human-like characteristics observed in the field of AI ethics and AI science communication.
Deroy, O, Topoi, 2023. DOI: 10.1007/s11245-023-09934-1.
Touching with the eyes: Oculomotor self-touch induces illusory body ownership
In this work, the authors hypothesise that proprioceptive information is not necessary for self-touch modulation of body-ownership.
Antonio Cataldo, Massimiliano Di Luca, Ophelia Deroy, Vincent Hayward, Cataldo et al., iScience 26, 106180, 2023. doi: 10.1016/j.isci.2023.106180
Trimmed helicoids: an architectured soft structure yielding soft robots with high precision, large workspace, and compliant interactions
In this work, the authors propose an architectured structure based on trimmed helicoids that allows for independent regulation of the bending and axial stiffness which facilitates tuneability of the resulting soft robot properties.
Guan, Q., Stella, F., Della Santina, C. et al. Trimmed helicoids: an architectured soft structure yielding soft robots with high precision, large workspace, and compliant interactions. npj Robot 1, 4 (2023). doi: 10.1038/s44182-023-00004-7.
