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arXiv 2023年

作者： Balatti, Pietro Ozdamar, Idil Sirintuna, Doganay Fortini, Luca Leonori, Mattia Gandarias, Juan M. Ajoudani, Arash HRI2 Lab Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy Robotics and Mechatronics lab Systems Engineering and Automation Department University of Malaga Malaga Spain

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario. Copyright © 2023, The Authors. All rights reserved.

关键词： Robotic arms

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A Modular Framework for Flexible Planning in Human-Robot Collaboration

A Modular Framework for Flexible Planning in Human-Robot Col...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Valerio Belcamino Mariya Kilina Linda Lastrico Alessandro Carfì Fulvio Mastrogiovanni Department of Informatics Bioengineering Robotics and System Engineering TheEngineRoom University of Genoa Genoa Italy Cognitive Architecture for Collaborative Technologies Unit (CONTACT) Italian Institute of Technology Genoa Italy

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

This paper presents a comprehensive framework to enhance Human-Robot Collaboration (HRC) in real-world scenarios. It introduces a formalism to model articulated tasks, requiring cooperation between two agents, through a smaller set of primitives. Our implementation leverages Hierarchical Task Networks (HTN) planning and a modular multisensory perception pipeline, which includes vision, human activity recognition, and tactile sensing. To showcase the system’s scalability, we present an experimental scenario where two humans alternate in collaborating with a Baxter robot to assemble four pieces of furniture with variable components. This integration highlights promising advancements in HRC, suggesting a scalable approach for complex, cooperative tasks across diverse applications.

关键词： Measurement Runtime Shape Scalability Human-robot interaction Collaboration Robot sensing systems Planning Sensors Human activity recognition

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Does Precision Grip Research Extend to Unconstrained, Multidigit Grasping?

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Journal of neurophysiology 2025年第6期133卷 2025 May 12页

作者： Fabrizio Lepori Frieder Hartmann Kira Dehn Manuela Chessa Roland W Fleming Guido Maiello School of Psychology University of Southampton Southampton United Kingdom. Department of Experimental Psychology Justus Liebig University Giessen Giessen Germany. Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy. Center for Mind Brain and Behavior (CMBB) University of Marburg and Justus Liebig University Giessen Giessen Germany.

Most daily tasks require using our hands. Whether taking a sip from a glass or throwing a ball, we effortlessly select appropriate grasps. Yet, despite many possible hand configurations, most grasping research has focused on the finger-and-thumb 'precision grip'. We thus questioned whether findings on precision grip-such as sensitivity to object mass and material configuration-hold under unconstrained grasping conditions. To test this, we compared how participants grasped 3D objects made of brass and wood, with both precision grip and unconstrained grasps. When unconstrained, participants rarely selected precision grips, favoring multi-digit grasps. Nevertheless, in both conditions, participants shifted grasps towards the objects' center of mass and, when grasp factors conflicted, the variability in their selections increased, indicating greater uncertainty about the optimal strategy. Further, despite favoring multidigit grasps, participants consistently placed the thumb and index finger on the same positions on the objects, suggesting that in multidigit grasps, the additional fingers primarily provided support. Our findings thus reveal that object material affects unconstrained grasping similarly to precision grip and imply that previous precision grip research may extend to unconstrained, multidigit conditions.

关键词： Grasping Material Configuration Multidigit Grasp Precision Grip

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A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research

arXiv

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arXiv 2024年

作者： Salazar, Teresa Araújo, Helder Cano, Alberto Abreu, Pedro Henriques Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra3030-790 Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3030-790 Portugal Virginia Commonwealth University RichmondVA23284 United States

Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple devices or organizations without sharing raw data, amplifies the need for fairness due to the heterogeneous data distributions across clients, which can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 47 research works specifically dedicated to addressing this issue. However, no dedicated survey has focused comprehensively on group fairness in federated learning. In this work, we present an in-depth survey on this topic, addressing the critical challenges and reviewing related works in the field. We create a novel taxonomy of these approaches based on key criteria such as data partitioning, location, and applied strategies. Additionally, we explore broader concerns related to this problem and investigate how different approaches handle the complexities of various sensitive groups and their intersections. Finally, we review the datasets and applications commonly used in current research. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated *** Codes 68T01 © 2024, CC BY.

关键词： Federated learning

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Evaluating leadership roles in human-robot interaction via highly dynamic collaborative tasks

Evaluating leadership roles in human-robot interaction via h...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Francesco Tassi Gustavo J. G. Lahr Doganay Sirintuna Arash Ajoudani Human-Robot Interfaces and Interaction Lab Istituto Italiano di Tecnologia Genoa Italy RAISE Ecosystem Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

To enable a comprehensive human-robot interaction, it is essential to refer to human-human collaboration and decode complex non-verbal communication aspects that are essential for adaptive decision-making and task success. Indeed, for a robust collaboration, it is useful to understand the intricacies and complexities of human communication during human-human interaction and to compare it with the human-robot interaction case. We study this communication exchange and information flow by evaluating the leader/follower behavior during physical interaction with different agents and different control types, focusing on non-verbal cues, to identify collaborative or competitive attitudes. To achieve this, we consider a dynamic task of collaboratively catching a falling object, which, by its nature, favors non-verbal communication channels. Multiple subjects performed the same task with different collaborative agents (i.e., human and robot) and with different control modalities, to evaluate the leadership roles and their implication on task success (successfully catching the object while minimizing impact forces). We analyze how the impact force minimization induced by the velocity matching optimal planner affects the catching success rate. The information flow is analyzed, and the leadership roles are identified. Further qualitative data is gathered from questionnaires and compared with respect to the analytic results.

关键词： Measurement Leadership Force Collaboration Human-robot interaction Focusing Robot sensing systems Minimization Market research Object recognition

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Feasibility of an Adaptive Upper Limb Robotic Training Protocol for Persons with Multiple Sclerosis

Feasibility of an Adaptive Upper Limb Robotic Training Proto...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Valentina Massone Kailynn Mannella Giulia A. Albanese Maura Casadio Jacopo Zenzeri Michael W. R. Holmes ReWing s.r.l. Milano Italy Department of Informatics Bioengineering Robotics and Systems Engineering Università Degli Studi di Genova Genova Italy Faculty of Applied Health Sciences Brock University St. Catharines ON Canada

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Lately, the use of robotic devices for tailored rehabilitation as well as strategies for assessing and monitoring sensorimotor function has garnered considerable interest. This work presents an innovative upper limb robotic training protocol for Persons with Multiple Sclerosis (PwMS), who can benefit from this type of rehabilitation by improving motor control and muscular strength and, consequently, quality of life. Participants took part in the study three times a week for a period that lasted six weeks on average. The task proposed for the robot-based training was tracking with a resistive force along the directions of wrist flexion-extension and radial-ulnar deviation. The effects of the training were investigated through robotic assessments and strength measurements conducted both before and after the intervention on both the trained and untrained limbs of participants. Results of the robotic assessments showed greater impairment in wrist position sense in the group of PwMS compared with a group of non-MS controls. However, there was a significant improvement from pre- to post-training in the tracing skills in both the MS and non-MS groups. Strength measurements showed significant improvement from pre- to post-intervention in both groups for both the trained and untrained limbs. This type of robotic training protocol shows promise for improving upper limb motor control and muscular strength in PwMS.

关键词： Training Wrist Motor drives Protocols Multiple sclerosis Atmospheric measurements Pulse width modulation Biomedical measurement Robot sensing systems Particle measurements

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Robotic in-hand manipulation with relaxed optimization

arXiv

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arXiv 2024年

作者： Hammoud, Ali Belcamino, Valerio Huet, Quentin Carfi, Alessandro Khoramshahi, Mahdi Perdereau, Veronique Mastrogiovanni, Fulvio Sorbonne Universite Institut des Systemes Intelligents et de Robotique ISIR ParisF-75005 France TheEngineRoom Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy

Dexterous in-hand manipulation is a unique and valuable human skill requiring sophisticated sensorimotor interaction with the environment while respecting stability constraints. Satisfying these constraints with generated motions is essential for a robotic platform to achieve reliable in-hand manipulation skills. Explicitly modelling these constraints can be challenging, but they can be implicitly modelled and learned through experience or human demonstrations. We propose a learning and control approach based on dictionaries of motion primitives generated from human demonstrations. To achieve this, we defined an optimization process that combines motion primitives to generate robot fingertip trajectories for moving an object from an initial to a desired final pose. Based on our experiments, our approach allows a robotic hand to handle objects like humans, adhering to stability constraints without requiring explicit formalization. In other words, the proposed motion primitive dictionaries learn and implicitly embed the constraints crucial to the in-hand manipulation task. © 2024, CC BY.

关键词： Robotic arms

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Modelling and Compensation for Transmission Error of Timing Belt in Legged Robots

Modelling and Compensation for Transmission Error of Timing ...

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IEEE International Conference on robotics and Biomimetics

作者： Jingcheng Jiang Yifang Zhang Nikos G. Tsagarakis Humanoids and Human Centered Mechatronics (HHCM) Research Line of Istituto Italiano di Tecnologia (IIT) Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy

The timing belt transmission offers numerous advantages for legged robots, including high efficiency, impact absorption and large range of joint motion. However, the transmission error under high load remains challenging to locomotion control and further applications of belt transmission. Traditional linear models cannot effectively model the belt deformation under a wide range of tension variations due to the nonlinearity. In this paper, we propose a model of the compensation for the belt transmission error based on the pretension and torque of the pully. The adopted approach bypasses the complexity of elaborate physical model derivations, yielding a non-linear model for transmission system errors through straightforward fitting. Based on the proposed model, an error compensation control is investigated and tested with an one-DoF leg prototype of legged robot. The alignment between experimental results and theoretical analysis demonstrates the accuracy of the modeling and the effectiveness of the error compensation control method. The proposed model provides a convenient and straightforward solution to effectively compensate for the belt transmission errors in legged robots.

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Operational Application of Mathematical Modeling for Enhanced Wildfire Risk Management in Liguria

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IFAC-PapersOnLine 2024年第2期58卷 61-66页

作者： Francesco Baghino Emanuele Gissi Nicolò Perello Paolo Fiorucci CIMA Research Foundation Via A. Magliotto 2 17100 Savona Italy Corpo Nazionale dei Vigili del Fuoco Via Albertazzi 2 16100 Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via All’Opera Pia 13 16145 Genoa Italy

Wildfire events can cause serious damages to environment, buildings and infrastructures, and human lives. Fighting against these occurrences requires very big efforts for all the involved authorities, both in the management of firefighters and volunteers, and the assistance of citizens. Wildfire forecast and simulation models can help in the preparedness and response phases for a better management of the available resources. In the Liguria region both wildfire forecast and simulation are included in the wildfire risk management, with interesting application, as in the example of the Albenga wildfire.

关键词： Natural risk management Modelling simulation Scenario analysis Wildfire simulation Wildfire scenario

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Expressing and Inferring Action Carefulness in Human-to-Robot Handovers

Expressing and Inferring Action Carefulness in Human-to-Robo...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Linda Lastrico Nuno Ferreira Duarte Alessandro Carfí Francesco Rea Alessandra Sciutti Fulvio Mastrogiovanni José Santos-Victor Cognitive Architecture for Collaborative Technologies Unit (CONTACT) Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy Vislab Institute for Systems and Robotics (ISR|Lisboa) Superior Técnico Universidade de Lisboa Portugal

Implicit communication plays such a crucial role during social exchanges that it must be considered for a good experience in human-robot interaction. This work addresses implicit communication associated with the detection of physical properties, transport, and manipulation of objects. We propose an ecological approach to infer object characteristics from subtle modulations of the natural kinematics occurring during human object manipulation. Similarly, we take inspiration from human strategies to shape robot movements to be communica-tive of the object properties while pursuing the action goals. In a realistic HRI scenario, participants handed over cups - filled with water or empty - to a robotic manipulator that sorted them. We implemented an online classifier to differentiate careful/not careful human movements, associated with the cups' content. We compared our proposed “expressive” controller, which modulates the movements according to the cup filling, against a neutral motion controller. Results show that human kinematics is adjusted during the task, as a function of the cup content, even in reach-to-grasp motion. Moreover, the carefulness during the handover of full cups can be reliably inferred online, well before action completion. Finally, although questionnaires did not reveal explicit preferences from partici-pants, the expressive robot condition improved task efficiency.

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