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Marta Lorenzini

22 accepted papers

2026

Estimating Trust in Human-Robot Collaboration through Behavioral Indicators and Explainability

Giulio Campagna, Marta Lagomarsino, Marta Lorenzini, Dimitrios Chrysostomou, Matthias Rehm, Arash Ajoudani
ICRA 2026poster

Industry 5.0 focuses on human-centric collaboration between humans and robots, prioritizing safety, comfort, and trust. This study introduces a data-driven framework to assess trust using behavioral indicators. The framework employs a Preference-Based Optimization algorithm to generate trust-enhanci…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Assisting Gait Stability in Walking Aid Users Exploiting Biomechanical Variables Correlation

Andrea Fortuna, Marta Lorenzini, Younggeol Cho, Robin Arbaud, Stefano Filippo Castiglia, Mariano Serrao +3
RA-L 2025

Walking aids for individuals with musculoskeletal frailty or motor disabilities must ensure adequate physical support and assistance to their users. To this end, sensor-enabled human state monitoring and estimation are crucial. This paper proposes an innovative approach to assessing users' stability

Cited by 0Source ↗Scholar ↗
2025

Estimating Trust in Human-Robot Collaboration Through Behavioral Indicators and Explainability

Giulio Campagna, Marta Lagomarsino, Marta Lorenzini, Dimitrios Chrysostomou, Matthias Rehm, Arash Ajoudani
RA-L 2025

Industry 5.0 focuses on human-centric collaboration between humans and robots, prioritizing safety, comfort, and trust. This study introduces a data-driven framework to assess trust using behavioral indicators. The framework employs a Preference-Based Optimization algorithm to generate trust-enhanci

Cited by 3Source ↗Scholar ↗
2025

Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control

Robin Arbaud, Elisa Motta, Marco Domenico Avaro, Stefano Picinich, Marta Lorenzini, Arash Ajoudani
ICRA 2025

Partial hand amputations significantly affect the physical and psychosocial well-being of individuals, yet intuitive control of externally powered prostheses remains an open challenge. To address this gap, we developed a force-controlled prosthetic finger activated by electromyography (EMG) signals.

Cited by 0Source ↗Scholar ↗
2025

Physics-Informed Learning for Human Whole-Body Kinematics Prediction via Sparse IMUs

Cheng Guo, Giuseppe L'Erario, Giulio Romualdi, Mattia Leonori, Marta Lorenzini, Arash Ajoudani +1
IROS 2025

Accurate and physically feasible human motion prediction is crucial for safe and seamless human-robot collaboration. While recent advancements in human motion capture enable real-time pose estimation, the practical value of many existing approaches is limited by the lack of future predictions and co

Cited by 0Source ↗Scholar ↗
2024

A Personalizable Controller for the Walking Assistive omNi-Directional Exo-Robot (WANDER)

Andrea Fortuna, Marta Lorenzini, Mattia Leonori, Juan M. Gandarias, Pietro Balatti, Younggeol Cho +2
ICRA 2024poster

Preserving and encouraging mobility in the elderly and adults with chronic conditions is of paramount importance. However, existing walking aids are either inadequate to provide sufficient support to users’ stability or too bulky and poorly maneuverable to be used outside hospital environments. In a…

Cited by 3Source ↗Scholar ↗
2024

A User- and Slope-Adaptive Control Framework for a Walking Aid Robot

Younggeol Cho, Marta Lorenzini, Andrea Fortuna, Mattia Leonori, Arash Ajoudani
RA-L 2024

Walking aid robots have been developed for elderly people or patients facing difficulties while walking. However, most of them are designed only for flat ground, must rely on handles, and have drawbacks such as oscillation and lack of stability. The primary goal of this research is to improve the tr

Cited by 1Source ↗Scholar ↗
2024

An Ergo-Interactive Framework for Human-Robot Collaboration Via Learning From Demonstration

Zhiwei Liao, Marta Lorenzini, Mattia Leonori, Fei Zhao, Gedong Jiang, Arash Ajoudani
RA-L 2024

This work presents an ergonomic and interactive human-robot collaboration (HRC) framework, through which new collaborative skills are extracted from a one-shot human demonstration and learned through Riemannian dynamic movement primitives (DMP). The proposed framework responds to human-robot interac

Cited by 10Source ↗Scholar ↗
2024

Promoting Trust in Industrial Human-Robot Collaboration Through Preference-Based Optimization

Giulio Campagna, Marta Lagomarsino, Marta Lorenzini, Dimitrios Chrysostomou, Matthias Rehm, Arash Ajoudani
RA-L 2024

This letter proposes a novel theoretical framework for promoting trust in human-robot collaboration (HRC). The framework exploits Preference-Based Optimization (PBO) and focuses on three key interaction parameters: robot velocity profile, human-robot separation distance, and vertical proximity to th

Cited by 23Source ↗Scholar ↗
2023

Maximising Coefficiency of Human-Robot Handovers Through Reinforcement Learning

Marta Lagomarsino, Marta Lorenzini, Merryn Dale Constable, Elena De Momi, Cristina Becchio, Arash Ajoudani
RA-L 2023

Handing objects to humans is an essential capability for collaborative robots. Previous research works on human-robot handovers focus on facilitating the performance of the human partner and possibly minimising the physical effort needed to grasp the object. However, altruistic robot behaviours may

Cited by 19Source ↗Scholar ↗
2022

Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation

Elena Merlo, Edoardo Lamon, Fabio Fusaro, Marta Lorenzini, Alessandro Carfi, Fulvio Mastrogiovanni +1
ICRA 2022poster

Even though cobots have high potential in bringing several benefits in the manufacturing and logistic processes, their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to boost the fitness of the human workers to the allocated task…

Cited by 19Source ↗Scholar ↗
2022

Enhancing Flexibility and Adaptability in Conjoined Human-Robot Industrial Tasks with a Minimalist Physical Interface

Juan M. Gandarias, Pietro Balatti, Edoardo Lamon, Marta Lorenzini, Arash Ajoudani
ICRA 2022poster

This paper presents a physical interface for collaborative mobile manipulators in industrial manufacturing and logistics applications. The proposed work builds on our earlier MOCA-MAN interface, through which an operator could be physically coupled to a mobile manipulator to be assisted in performin…

Cited by 7Source ↗Scholar ↗
2022

Improving Standing Balance Performance through the Assistance of a Mobile Collaborative Robot

Francisco J. Ruiz-Ruiz, Alberto Giammarino, Marta Lorenzini, Juan M. Gandarias, Jesús H. Gómez-De-Gabriel, Arash Ajoudani
ICRA 2022poster

This paper presents the design and development of a robotic system to give physical assistance to the elderly or people with neurological disorders such as Ataxia or Parkin-son's. In particular, we propose using a mobile collaborative robot with an interaction-assistive whole-body interface to help…

Cited by 13Source ↗Scholar ↗
2022

Robot Trajectory Adaptation to Optimise the Trade-off between Human Cognitive Ergonomics and Workplace Productivity in Collaborative Tasks

Marta Lagomarsino, Marta Lorenzini, Elena De Momi, Arash Ajoudani
IROS 2022poster

In hybrid industrial environments, workers' comfort and positive perception of safety are essential requirements for successful acceptance and usage of collaborative robots. This paper proposes a novel human-robot interaction framework in which the robot behaviour is adapted online according to the…

Cited by 29Source ↗Scholar ↗
2021

A Flexible Robotics-Inspired Computational Model of Compressive Loading on the Human Spine

Leonardo Ventura, Marta Lorenzini, Wansoo Kim, Arash Ajoudani
RA-L 2021

The use of powerful robotics tools in modeling and analysis of complex biomechanical systems has led to the development of computationally effective and scalable human models. Our early work in this regard focused on a humanoids-based rigid-body modeling of human kinodynamics to monitor instantaneou

Cited by 8Source ↗Scholar ↗
2020

A Framework for Real-time and Personalisable Human Ergonomics Monitoring

Luca Fortini, Marta Lorenzini, Wansoo Kim, Elena De Momi, Arash Ajoudani
IROS 2020poster

The objective of this paper is to present a personalisable human ergonomics framework that integrates a method for real-time identification of a human model and an ergonomics monitoring function. The human model is based on a floating base structure and on a Statically Equivalent Serial Chain (SESC)…

Cited by 13Source ↗Scholar ↗
2019

A New Overloading Fatigue Model for Ergonomic Risk Assessment with Application to Human-Robot Collaboration

Marta Lorenzini, Wansoo Kim, Elena De Momi, Arash Ajoudani
ICRA 2019poster

Among the numerous risk factors associated to work-related musculoskeletal disorders (WMSD), repetitive and monotonous movements with light-weight tools are one of the most frequently cited. Such tasks may indeed result in the excessive accumulation of local muscle fatigue, causing severe injuries i…

Cited by 69Source ↗Scholar ↗
2019

A Teleoperation Interface for Loco-Manipulation Control of Mobile Collaborative Robotic Assistant

Yuqiang Wu, Pietro Balatti, Marta Lorenzini, Fei Zhao, Wansoo Kim, Arash Ajoudani
RA-L 2019

This letter presents a novel teleoperation interface that enables remote loco-manipulation control of a MObile Collaborative robotic Assistant (MOCA). MOCA is a new research platform developed at the Istituto Italiano di Tecnologia (IIT), which is composed of a lightweight manipulator arm, a Pisa/II

Cited by 91Source ↗Scholar ↗
2019

Towards Ergonomic Control of Collaborative Effort in Multi-human Mobile-robot Teams

Wansoo Kim, Marta Lorenzini, Pietro Balatti, Yuqiang Wu, Arash Ajoudani
IROS 2019poster

In this paper, we propose a control framework for a multi-human and mobile-robot collaborative team, that takes into account the co-workers' ergonomic requirements as well as the demand for high flexibility in the manufacturing industries. The new MObile Collaborative robotic Assistant (MOCA), which…

Cited by 38Source ↗Scholar ↗
2018

A Synergistic Approach to the Real-Time Estimation of the Feet Ground Reaction Forces and Centers of Pressure in Humans With Application to Human-Robot Collaboration

Marta Lorenzini, Wansoo Kim, Elena De Momi, Arash Ajoudani
RA-L 2018

This letter proposes a novel technique for the real-time estimation of the human feet ground reaction forces (GRFs) and centers of pressure (CoP) from the whole-body CoP and body configuration. The estimated variables are used for the estimation of the overloading torques in human joints during doub

Cited by 22Source ↗Scholar ↗
2018

ErgoTac: A Tactile Feedback Interface for Improving Human Ergonomics in Workplaces

Wansoo Kim, Marta Lorenzini, Kagan Kapicioglu, Arash Ajoudani
RA-L 2018

In this letter, we present a novel wearable feedback interface with the aim to improve human ergonomics in the execution of heavy or repetitive industrial tasks. The proposed interface incorporates a reduced-complexity and real-time model for the estimation of human joints' load variations, based on

Cited by 46Source ↗Scholar ↗
2018

The CoDyCo Project Achievements and Beyond: Toward Human Aware Whole-Body Controllers for Physical Human Robot Interaction

Francesco Romano, Gabriele Nava, Morteza Azad, Jernej Camernik, Stefano Dafarra, Oriane Dermy +12
RA-L 2018

The success of robots in real-world environments is largely dependent on their ability to interact with both humans and said environment. The FP7 EU project CoDyCo focused on the latter of these two challenges by exploiting both rigid and compliant contacts dynamics in the robot control problem. Reg

Cited by 32Source ↗Scholar ↗