arXiv subjects

Stefano Baraldo

Publications and source records attributed to Stefano Baraldo.

6 recordsLinked to original sources

arXiv2026

EBuddy: a workflow orchestrator for industrial human-machine collaboration

This paper presents EBuddy, a voice-guided workflow orchestrator for natural human-machine collaboration in industrial environments. EBuddy targets a recurrent bottleneck in tool-intensive workflows: expert know-how is effective but difficult to scale, and execution quality degrades when procedures are reconstructed ad hoc across operators and sessions. EBuddy operationalizes expert practice as a finite state machine (FSM) driven application that provides an interpretable decision frame at runtime (current state and admissible actions), so that spoken requests are interpreted within state-grounded constraints, while the system executes and monitors the corresponding tool interactions. Through modular workflow artifacts, EBuddy coordinates heterogeneous resources, including GUI-driven software and a collaborative robot, leveraging fully voice-based interaction through automatic speech recognition and intent understanding. An industrial pilot on impeller blade inspection and repair preparation for directed energy deposition (DED), realized by human-robot collaboration, shows substantial reductions in end-to-end process duration across onboarding, 3D scanning and processing, and repair program generation, while preserving repeatability and low operator burden.

cs.RO↗

arXiv2025

DynaMimicGen: A Data Generation Framework for Robot Learning of Dynamic Tasks

Learning robust manipulation policies typically requires large and diverse datasets, the collection of which is time-consuming, labor-intensive, and often impractical for dynamic environments. In this work, we introduce DynaMimicGen (D-MG), a scalable dataset generation framework that enables policy training from minimal human supervision while uniquely supporting dynamic task settings. Given only a few human demonstrations, D-MG first segments the demonstrations into meaningful sub-tasks, then leverages Dynamic Movement Primitives (DMPs) to adapt and generalize the demonstrated behaviors to novel and dynamically changing environments. Improving prior methods that rely on static assumptions or simplistic trajectory interpolation, D-MG produces smooth, realistic, and task-consistent Cartesian trajectories that adapt in real time to changes in object poses, robot states, or scene geometry during task execution. Our method supports different scenarios - including scene layouts, object instances, and robot configurations - making it suitable for both static and highly dynamic manipulation tasks. We show that robot agents trained via imitation learning on D-MG-generated data achieve strong performance across long-horizon and contact-rich benchmarks, including tasks like cube stacking and placing mugs in drawers, even under unpredictable environment changes. By eliminating the need for extensive human demonstrations and enabling generalization in dynamic settings, D-MG offers a powerful and efficient alternative to manual data collection, paving the way toward scalable, autonomous robot learning.

cs.RO↗

arXiv2025

MultiPhysio-HRC: Multimodal Physiological Signals Dataset for industrial Human-Robot Collaboration

Human-robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants' mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset's potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems.

cs.RO↗

arXiv2025

Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning

With the advent of Industry 5.0, manufacturers are increasingly prioritizing worker well-being alongside mass customization. Stress-aware Human-Robot Collaboration (HRC) plays a crucial role in this paradigm, where robots must adapt their behavior to human mental states to improve collaboration fluency and safety. This paper presents a novel framework that integrates Federated Learning (FL) to enable personalized mental state evaluation while preserving user privacy. By leveraging physiological signals, including EEG, ECG, EDA, EMG, and respiration, a multimodal model predicts an operator's stress level, facilitating real-time robot adaptation. The FL-based approach allows distributed on-device training, ensuring data confidentiality while improving model generalization and individual customization. Results demonstrate that the deployment of an FL approach results in a global model with performance in stress prediction accuracy comparable to a centralized training approach. Moreover, FL allows for enhancing personalization, thereby optimizing human-robot interaction in industrial settings, while preserving data privacy. The proposed framework advances privacy-preserving, adaptive robotics to enhance workforce well-being in smart manufacturing.

cs.RO↗

arXiv2025

ros2 fanuc interface: Design and Evaluation of a Fanuc CRX Hardware Interface in ROS2

This paper introduces the ROS2 control and the Hardware Interface (HW) integration for the Fanuc CRX- robot family. It explains basic implementation details and communication protocols, and its integration with the Moveit2 motion planning library. We conducted a series of experiments to evaluate relevant performances in the robotics field. We tested the developed ros2_fanuc_interface for four relevant robotics cases: step response, trajectory tracking, collision avoidance integrated with Moveit2, and dynamic velocity scaling, respectively. Results show that, despite a non-negligible delay between command and feedback, the robot can track the defined path with negligible errors (if it complies with joint velocity limits), ensuring collision avoidance. Full code is open source and available at https://github.com/paolofrance/ros2_fanuc_interface.

cs.RO↗

arXiv2025

Human-robot collaborative transport personalization via Dynamic Movement Primitives and velocity scaling

Nowadays, industries are showing a growing interest in human-robot collaboration, particularly for shared tasks. This requires intelligent strategies to plan a robot's motions, considering both task constraints and human-specific factors such as height and movement preferences. This work introduces a novel approach to generate personalized trajectories using Dynamic Movement Primitives (DMPs), enhanced with real-time velocity scaling based on human feedback. The method was rigorously tested in industrial-grade experiments, focusing on the collaborative transport of an engine cowl lip section. Comparative analysis between DMP-generated trajectories and a state-of-the-art motion planner (BiTRRT) highlights their adaptability combined with velocity scaling. Subjective user feedback further demonstrates a clear preference for DMP- based interactions. Objective evaluations, including physiological measurements from brain and skin activity, reinforce these findings, showcasing the advantages of DMPs in enhancing human-robot interaction and improving user experience.

cs.RO↗