Mattia Leonori

Luca Fortini, Mattia Leonori, Juan M. Gandarias et al.

Simulation tools are essential for robotics research, especially for those domains in which safety is crucial, such as Human-Robot Collaboration (HRC). However, it is challenging to simulate human behaviors, and existing robotics simulators do not integrate functional human models. This work presents Open-VICO, an open-source toolkit to integrate virtual human models in Gazebo focusing on vision-based human tracking. In particular, Open-VICO allows to combine in the same simulation environment realistic human kinematic models, multi-camera vision setups, and human-tracking techniques along with numerous robot and sensor models thanks to Gazebo. The possibility to incorporate pre-recorded human skeleton motion with Motion Capture systems broadens the landscape of human performance behavioral analysis within Human-Robot Interaction (HRI) settings. To describe the functionalities and stress the potential of the toolkit four specific examples, chosen among relevant literature challenges in the field, are developed using our simulation utils: i) 3D multi-RGB-D camera calibration in simulation, ii) creation of a synthetic human skeleton tracking dataset based on OpenPose, iii) multi-camera scenario for human skeleton tracking in simulation, and iv) a human-robot interaction example. The key of this work is to create a straightforward pipeline which we hope will motivate research on new vision-based algorithms and methodologies for lightweight human-tracking and flexible human-robot applications.

Luca Fortini, Mattia Leonori, Juan M. Gandarias et al.

Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Traditional approaches involve attaching artificial fiducial objects or sensors to the body, limiting their usability and comfort-of-use and consequently narrowing their application fields. Recent advances in Artificial Intelligence (AI) have allowed for markerless solutions. However, most of these methods operate in 2D, while those providing 3D solutions compromise accuracy and real-time performance. To address this challenge and unlock the potential of visual pose estimation methods in real-world scenarios, we propose a markerless framework that combines multi-camera views and 2D AI-based pose estimation methods to track 3D human motion. Our approach integrates a Weighted Least Square (WLS) algorithm that computes 3D human motion from multiple 2D pose estimations provided by an AI-driven method. The method is integrated within the Open-VICO framework allowing simulation and real-world execution. Several experiments have been conducted, which have shown high accuracy and real-time performance, demonstrating the high level of readiness for real-world applications and the potential to revolutionize human motion capture.

Cheng Guo, Giuseppe L'Erario, Giulio Romualdi et al.

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 consideration of physical constraints. Conventional motion prediction schemes rely heavily on past poses, which are not always available in real-world scenarios. To address these limitations, we present a physics-informed learning framework that integrates domain knowledge into both training and inference to predict human motion using inertial measurements from only 5 IMUs. We propose a network that accounts for the spatial characteristics of human movements. During training, we incorporate forward and differential kinematics functions as additional loss components to regularize the learned joint predictions. At the inference stage, we refine the prediction from the previous iteration to update a joint state buffer, which is used as extra inputs to the network. Experimental results demonstrate that our approach achieves high accuracy, smooth transitions between motions, and generalizes well to unseen subjects

Mattia Leonori, Juan M. Gandarias, Arash Ajoudani

Safety is one of the most fundamental aspects of robotics, especially when it comes to collaborative robots (cobots) that are expected to physically interact with humans. Although a large body of literature has focused on safety-related aspects for fixed-based cobots, a low effort has been put into developing collaborative mobile manipulators. In response to this need, this work presents MOCA-S, i.e., Sensitive Mobile Collaborative Robotic Assistant, that integrates a low-cost, capacitive tactile cover to measure interaction forces applied to the robot base. The tactile cover comprises a set of 11 capacitive large-area tactile sensors distributed as a 1-D tactile array around the base. Characterization of the tactile sensors with different materials is included. Moreover, two expanded whole-body controllers that exploit the platform's tactile cover and the loco-manipulation features are proposed. These controllers are tested in two experiments, demonstrating the potential of MOCA-S for safe physical Human-Robot Interaction (pHRI). Finally, an experiment is carried out in which an undesired collision occurs between MOCA-S and a human during a loco-manipulation task. The results demonstrate the intrinsic safety of MOCA-S and the proposed controllers, suggesting a new step towards creating safe mobile manipulators.

Alberto Giammarino, Juan M. Gandarias, Pietro Balatti et al.

This paper presents a mobile supernumerary robotic approach to physical assistance in human-robot conjoined actions. The study starts with a description of the SUPER-MAN concept. The idea is to develop and utilize mobile collaborative systems that can follow human loco-manipulation commands to perform industrial tasks through three main components: i) an admittance-type interface, ii) a human-robot interaction controller, and iii) a supernumerary robotic body. Next, we present two possible implementations within the framework from theoretical and hardware perspectives. The first system is called MOCA-MAN and comprises a redundant torque-controlled robotic arm and an omnidirectional mobile platform. The second one is called Kairos-MAN, formed by a high-payload 6-DoF velocity-controlled robotic arm and an omnidirectional mobile platform. The systems share the same admittance interface, through which user wrenches are translated to loco-manipulation commands generated by whole-body controllers of each system. Besides, a thorough user study with multiple and cross-gender subjects is presented to reveal the quantitative performance of the two systems in effort-demanding and dexterous tasks. Moreover, we provide qualitative results from the NASA-TLX questionnaire to demonstrate the SUPER-MAN approach's potential and its acceptability from the users' viewpoint.