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arXiv 2022年

作者： Fiorentino, Maria Chiara Villani, Francesca Pia Di Cosmo, Mariachiara Frontoni, Emanuele Moccia, Sara Department Of Information Engineering Università Politecnica Delle Marche Italy Department Of Humanities Università Degli Studi Di Macerata Italy Department Of Political Sciences Communication And International Relations Università Degli Studi Di Macerata Italy Biorobotics Institute Department Of Excellence In Robotics & AI Scuola Superiore Sant'Anna Italy

Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite the large number of survey papers already present in this field, most of them are focusing on a broader area of medicalimage analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 145 research papers published after 2017. Each paper is analyzed and commented from both the methodology and application perspective. We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice. © 2022, CC BY-NC-SA.

关键词： Surveys

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Enhanced HW-NAS for Affordance Segmentation on Wearable robotics

Enhanced HW-NAS for Affordance Segmentation on Wearable Robo...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Edoardo Ragusa Rodolfo Zunino Claudio Loconsole Antonio Frisoli Paolo Gastaldo DITEN University of Genoa Genova Italy Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy Scuola Superiore Sant'Anna Institute of Mechanical Intelligence Ghezzano Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

The automatic design of deep neural network architectures executed in real-time on mobile processors would pave the way for new developments in wearable robotics. Processing information from cameras in real-time is essential to implement semiautonomous control pipelines. This work presents a hardware-aware neural architecture search suitable to generate architectures for affordance segmentation supported by mobile processors. The procedure uses a weight-sharing mechanism to speed up the search procedure and improves the convergence capability of the network selection procedure. In addition, the proposed search space has been design to induce multi-resolution features. These factors allow the network generation procedure to select architectures with a better trade-off between generalization performance and hardware requirements when compared to existing solutions.

关键词： Program processors Affordances Robot vision systems Process control Wearable robots Aerospace electronics Real-time systems Hardware Neural architecture search Convergence

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Model Order Reduction of the Doyle-Fuller-Newman Model via Proper Orthogonal Decomposition and Optimal Collocation

Model Order Reduction of the Doyle-Fuller-Newman Model via P...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Gianluca Manduca Zhaoxuan Zhu Polina B. Ringler Guodong Fan Marcello Canova The BioRobotics Institute & Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Autonomy Research Motional Boston MA USA Dep. of Mechanical Engineering Colorado School of Mines Golden CO USA School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Center for Automotive Research The Ohio State University Columbus OH USA

Adapting physics-based models for digital twin frameworks, estimation and control of Lithium ion batteries requires a significant reduction of the computational complexity of electrochemical models without sacrificing accuracy. In this study, we propose a model order reduction technique for the Doyle-Fuller-Newman model of a battery cell that integrates Proper Orthogonal Decomposition with an optimized orthogonal collocation method. Simulation results indicate that the reduced-order model is faster than the full-order model, reducing computation time by up to 99% while maintaining negligible voltage discrepancy with a root mean square error (RMSE) of less than 0.005V. The proposed approach provides a powerful framework for improving the computation time of complex, physics-based models based on coupled partial differential equations, leading to more accurate predictions and better design and optimization of complex components and processes.

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Flexos: A Portable, SEA-Based Shoulder Exoskeleton with Hyper-redundant Kinematics for Weight Lifting Assistance

Flexos: A Portable, SEA-Based Shoulder Exoskeleton with Hype...

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World Haptics Conference

作者： Gianluca Rinaldi Luca Tiseni Michele Xiloyannis Lorenzo Masia Antonio Frisoli Domenico Chiaradia Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Institute of Mechanical Intelligence Pisa Italy Akina AG Zürich Switzerland Institut für Technische Informatik (ZITI) Heidelberg University Heidelberg Germany

In this paper, we present Flexos, a fully portable shoulder exoskeleton developed for logistic and industrial tasks. The device presents a simplified design with only a single, series elastic actuated joint employing an active torque control that assists the user in performing weight-lifting tasks. The device is lightweight and can be easily worn as a backpack. Ergonomic-shaped human-robot interfaces have been custom designed and 3D printed in order to achieve maximum comfort for the wearer. The exoskeleton has a hyper-redundant kinematic chain - a flexible link-connecting the backpack to the main series elastic actuator, which exerts torque on the upper arm. The SEA output torque is set by a closed-loop controller, whose aim is to compensate for gravity torque due to the human arm and weights lifted by the user. The device allows covering almost all the shoulder range of motion − 89.2% flexion/extension, and 88.4% internal/external rotation. Seven healthy participants were involved in a pilot study to assess the reduction of muscular activity - sEMG - in five targeted muscles during static and dynamic weight-lifting tasks. The assistance provided by the exoskeleton resulted in an average (−31.6% static, −24.6% dynamic) reduction of muscular effort.

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Decoding Upper-Limb Movement Intention Through Adaptive Dynamic Movement Primitives: A Proof-of-Concept Study with a Shoulder-Elbow Exoskeleton

Decoding Upper-Limb Movement Intention Through Adaptive Dyna...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Michele Francesco Penna Emilio Trigili Lorenzo Amato Huseyin Eken Filippo Dell'Agnello Francesco Lanotte Emanuele Gruppioni Nicola Vitiello Simona Crea The BioRobotics Institute Scuola Superiore Sant'Anna Pontedera Italy Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy The BioRobotics Institute Pontedera Italy Department of Physical Medicine and Rehabilitation Northwestern University Chicago IL USA Max Lader Lab for Rehabilitation Technologies and Outcomes Research Shirley Ryan AbilityLab Chicago IL USA Centro Protesi Inail di Vigorso di Budrio Bologna Italy IRCCS Fondazione Don Gnocchi Florence Italy IUVO S.r.l. a spinoff company of Scuola Superiore Sant'Anna

This work presents an intention decoding algorithm that can be used to control a 4 degrees-of-freedom shoulder-elbow exoskeleton in reaching tasks. The algorithm was designed to assist the movement of users with upper-limb impairments who can initiate the movement by themselves. It relies on the observation of the initial part of the user's movement through joint angle measures and aims to estimate in real-time the phase of the movement and predict the goal position of the hand in the reaching task. The algorithm is based on adaptive Dynamic Movement Primitives and Gaussian Mixture Models. The performance of the algorithm was verified in robot-assisted planar reaching movements performed by one healthy subject wearing the exoskeleton. Tests included movements of different amplitudes and orientations. Results showed that the algorithm could predict the hand's final position with an error lower than 5 cm after 0.25 s from the movement onset, and that the final position reached during the tests was on average less than 4 cm far from the target position. Finally, the effects of the assistance were observed in a reduction of the activation of the Biceps Brachii and of the time to execute the reaching tasks.

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On-Device Neural Architecture Search

On-Device Neural Architecture Search

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Andrea Mattia Garavagno Edoardo Ragusa Paolo Gastaldo Antonio Frisoli Claudio Loconsole Department of Electrical Electronic Telecommunication Engineering and Naval Architecture DITEN University of Genoa Genoa Italy Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy Institute of Mechanical Intelligence Scuola Superiore Sant'Anna Pisa Italy Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired through sensors. This new adaptation capability can be particularly useful in the case of human-machine interfaces for which the neural network analyzing the biometrical data can be re-designed each time the user changes, after a guided data collection procedure, fighting the typical data variations between individuals on a new level. To implement the proposed approach a new NAS has been designed and then validated on the Italian Sign Language dataset (ISL), a collection of surface electromyography (sEMG) signals of the signs of the Italian alphabet, using several embedded systems. Moreover, further validation on the Case Western Reserve University dataset (CWRU), a benchmark for intelligent fault diagnosis, is presented to suggest another possible application of the proposed approach. When run on a Raspberry Pi 4, the proposed NAS performs beyond the state of the art proposing a tiny neural architecture having 0.63 times less RAM occupancy and 5.96 percentage points of more accuracy in the case of the ISL dataset; and 0.44 times less RAM occupancy and 0.2 percentage points of more accuracy in the case of the CWRU dataset.

关键词： Performance evaluation Fault diagnosis Biometrics Sign language Costs Human-machine systems Random access memory Computer architecture Real-time systems Neural architecture search

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Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors

arXiv

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arXiv 2025年

作者： Sun, Yuhao Zhang, Shixin Li, Wenzhuang Zhao, Jie Shan, Jianhua Shen, Zirong Chen, Zixi Sun, Fuchun Guo, Di Fang, Bin School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100083 China Beijing100083 China School of International Beijing University of Posts and Telecommunications Beijing100876 China School of Mechanical Engineering Anhui University of Technology China Zhili College Tsinghua University Beijing100084 China Biorobotics Institute The Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa56127 Italy Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

With the development of robotics technology, some tactile sensors, such as vision-based sensors, have been applied to contact-rich robotics tasks. However, the durability of vision-based tactile sensors significantly increases the cost of tactile information acquisition. Utilizing simulation to generate tactile data has emerged as a reliable approach to address this issue. While data-driven methods for tactile data generation lack robustness, finite element methods (FEM) based approaches require significant computational costs. To address these issues, we integrated a pinhole camera model into the low computational cost vision-based tactile simulator Tacchi that used the Material Point Method (MPM) as the simulated method, completing the simulation of marker motion images. We upgraded Tacchi and introduced Tacchi 2.0. This simulator can simulate tactile images, marked motion images, and joint images under different motion states like pressing, slipping, and rotating. Experimental results demonstrate the reliability of our method and its robustness across various vision-based tactile sensors. © 2025, CC BY.

关键词： Pinhole cameras

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Semantization of memories in a hippocampal–cortical spiking neural network

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Neurocomputing 2025年 640卷

作者： D'Alba, Federico Kushawaha, Nilay Fruzzetti, Lorenzo Paolucci, Pier Stanislao Falotico, Egidio The BioRobotics Institute Scuola Superiore Sant'Anna Pontedera Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy CNR - Consiglio Nazionale Ricerche Pisa Italy Roma Italy

The human brain consolidates episodic memories into more semantic representations during sleep, enabling the continuous integration of new knowledge. This transformation is supported by hippocampal replays, which triggers a reactivation and reshaping of synaptic connections in the neocortex. In this study, we developed a plastic spiking neural network of Leaky Integrate-and-Fire (LIF) neurons to simulate the interaction between the hippocampus, perceptual neocortical areas, and neocortical regions responsible for processing semantic and contextual information. The model operates in two learning phases: first, the network encodes new experiences as episodic memories;then, during a sleep-like phase, hippocampal reactivation propagates to the neocortex. Crucially, the model leverages the apical mechanisms recently proposed by the experimentally grounded Dendritic Integration Theory (DIT) to modulate the activity of neural assemblies during both learning and replay. We evaluated the model on continual learning tasks, including split and rotational MNIST, and further demonstrated its practical applicability by training and testing it on sensory data from a soft pneumatic gripper in a dynamic robotic environment. © 2025 The Authors

关键词： Integration

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Preliminary Minimum Reporting Requirements for In-Vivo Neural Interface Research: I. Implantable Neural Interfaces

IEEE Open Journal of Engineering in Medicine and Biology

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IEEE Open Journal of Engineering in Medicine and Biology 2021年 2卷 74-83页

作者： Eiber, Calvin Delbeke, Jean Cardoso, Jorge De Neeling, Martijn John, Sam Lee, Chang Won Skefos, Jerry Sun, Argus Prodanov, Dimiter Mckinney, Zach University of Melbourne MelbourneVIC Australia Ghent University Ghent Belgium Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal KU Leuven Leuven Belgium Hyundai MOBIS Seoul Korea Republic of MetaCell BostonMA United States University of California Los AngelesCA United States NeuroElectronics Research Flanders Imec Leuven Belgium BioRobotics Institute and Center for Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy

The pace of research and development in neuroscience, neurotechnology, and neurorehabilitation is rapidly accelerating, with the number of publications doubling every 4.2 years. Maintaining this progress requires technological standards and scientific reporting guidelines to provide frameworks for communication and interoperability. The present lack of such neurotechnology standards limits the transparency, repro-ducibility, and meta-analysis of this growing body of literature, posing an ongoing barrier to research, clinical, and commercial objectives. Continued neurotechnological innovation requires the development of some minimal standards to promote integration between this broad spectrum of technologies and therapies. To preserve design freedom and accelerate the translation of research into safe and effective technologies with maximal user benefit, such standards must be collaboratively co-developed by the full range of neuroscience and neurotechnology stakeholders. This paper summarizes the preliminary recommendations of IEEE P2794 Standards Working Group, developing a Reporting Standard for in-vivo Neural Interface Research (RSNIR). © 2020 IEEE.

关键词： Standardization

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Benchmarking Multi-Object Grasping

arXiv

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arXiv 2025年

作者： Chen, Tianze Frumento, Ricardo Pagnanelli, Giulia Cei, Gianmarco Keth, Villa Gafarov, Shahaddin Gong, Jian Ye, Zihe Baracca, Marco D’Avella, Salvatore Bianchi, Matteo Sun, Yu of Computer Science and Engineering Department University of South Florida TampaFL33620 United States Research Center"E. Piaggio" Department of Information Engineering University of Pisa Pisa Italy Rutgers University New BrunswickNJ08901 United States Department of Excellence in Robotics & AI Mechanical Intelligence Institute Scuola Superiore Sant’Anna Pisa Italy

In this work, we describe a multi-object grasping benchmark to evaluate the grasping and manipulation capabilities of robotic systems in both pile and surface scenarios. The benchmark introduces three robot multi-object grasping benchmarking protocols designed to challenge different aspects of robotic manipulation. These protocols are: 1) the Only-Pick-Once protocol, which assesses the robot’s ability to efficiently pick multiple objects in a single attempt;2) the Accurate pick-trnsferring protocol, which evaluates the robot’s capacity to selectively grasp and transport a specific number of objects from a cluttered environment;and 3) the Pick-transferring-all protocol, which challenges the robot to clear an entire scene by sequentially grasping and transferring all available objects. These protocols are intended to be adopted by the broader robotics research community, providing a standardized method to assess and compare robotic systems’ performance in multi-object grasping tasks. We establish baselines for these protocols using standard planning and perception algorithms on a Barrett hand, Robotiq parallel jar gripper, and the Pisa/IIT Softhand-2, which is a soft underactuated robotic hand. We discuss the results in relation to human performance in similar tasks we well. © 2025, CC BY-NC-ND.

关键词： Robot programming

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