Author name cluster

Dario Piga

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7 papers

2 author rows

JBHI Journal 2025 Journal Article

Contactless Estimation of Respiratory Frequency Using 3D-CNN on Thermal Images

Federica Gioia
Filippo Pura
Alberto Greco
Dario Piga
Arcangelo Merla
Marco Forgione

Monitoring physiological parameters such as respiratory rate (f $_{R}$ ) is essential for diagnosing and managing various pathological conditions. Thermal imaging offers a promising contactless alternative to traditional methods, which often rely on partially invasive sensors or obtrusive wearable systems. However, existing approaches for f $_{R}$ estimation from thermal signals typically require extensive pre-processing and manual or semi-automatic region-of-interest (ROI) tracking, limiting their practical applicability. This study proposes a deep learning-based method for estimating f $_{R}$ directly from thermal videos, eliminating the need for complex pre-processing and ROI tracking. A 3D Convolutional Neural Network (3D-CNN) is developed to operate on raw thermal video data. To address challenges related to small datasets, the model is trained using data augmentation and transfer learning from synthetic datasets. Experimental results demonstrate that the proposed approach achieves a validation $R^{2}$ score of approximately 0. 61 on both pre-processed and raw thermal videos. By simplifying the workflow, this method holds promise for enhancing the feasibility of thermal imaging in real-world applications, such as remote healthcare and driver monitoring in automotive applications.

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UAI Conference 2023 Conference Paper

Learning Choice Functions with Gaussian Processes

Alessio Benavoli
Dario Azzimonti
Dario Piga

In consumer theory, ranking available objects by means of preference relations yields the most common description of individual choices. However, preference-based models assume that individuals: (1) give their preferences only between pairs of objects; (2) are always able to pick the best preferred object. In many situations, they may be instead choosing out of a set with more than two elements and, because of lack of information and/or incomparability (objects with contradictory characteristics), they may not be able to select a single most preferred object. To address these situations, we need a choice model which allows an individual to express a set-valued choice. Choice functions provide such a mathematical framework. We propose a Gaussian Process model to learn choice functions from choice data. The model assumes a multiple utility representation of a choice function based on the concept of Pareto rationalization, and derives a strategy to learn both the number and the values of these latent multiple utilities. Simulation experiments demonstrate that the proposed model outperforms the state-of-the-art methods.

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IJCAI Conference 2023 Conference Paper

Q-Learning-Based Model Predictive Variable Impedance Control for Physical Human-Robot Collaboration (Extended Abstract)

Loris Roveda
Andrea Testa
Asad Ali Shahid
Francesco Braghin
Dario Piga

Physical human-robot collaboration is increasingly required in many contexts. To implement an effective collaboration, the robot should be able to recognize the human’s intentions and guarantee safe and adaptive behavior along the intended motion directions. The robot-control strategies with such attributes are particularly demanded in the industrial field. Indeed, with this aim, this work proposes a Q-Learning-based Model Predictive Variable Impedance Control (Q-LMPVIC) to assist the operators in physical human-robot collaboration (pHRC) tasks. A Cartesian impedance control loop is designed to implement decoupled compliant robot dynamics. The impedance control parameters (i. e. , setpoint and damping parameters) are then optimized online in order to maximize the performance of the pHRC. For this purpose, an ensemble of neural networks is designed to learn the modeling of the human-robot interaction dynamics while capturing the associated uncertainties. The derived modeling is then exploited by the model predictive controller (MPC), enhanced with stability guarantees by means of Lyapunov constraints. The MPC is solved by making use of a Q-Learning method that, in its online implementation, uses an actor-critic algorithm to approximate the exact solution. Indeed, the Q-learning method provides an accurate and highly efficient solution (in terms of computational time and resources). The proposed approach has been validated through experimental tests, in which a Franka EMIKA panda robot has been used as a test platform.

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AIJ Journal 2022 Journal Article

Q-Learning-based model predictive variable impedance control for physical human-robot collaboration

Loris Roveda
Andrea Testa
Asad Ali Shahid
Francesco Braghin
Dario Piga

Physical human-robot collaboration is increasingly required in many contexts (such as industrial and rehabilitation applications). The robot needs to interact with the human to perform the target task while relieving the user from the workload. To do that, the robot should be able to recognize the human's intentions and guarantee safe and adaptive behavior along the intended motion directions. The robot-control strategies with such attributes are particularly demanded in the industrial field, where the operator guides the robot manually to manipulate heavy parts (e. g. , while teaching a specific task). With this aim, this work proposes a Q-Learning-based Model Predictive Variable Impedance Control (Q-LMPVIC) to assist the operators in a physical human-robot collaboration (pHRC) tasks. A Cartesian impedance control loop is designed to implement a decoupled compliant robot dynamics. The impedance control parameters (i. e. , setpoint and damping parameters) are then optimized online in order to maximize the performance of the pHRC. For this purpose, an ensemble of neural networks is designed to learn the modeling of the human-robot interaction dynamics while capturing the associated uncertainties. The derived modeling is then exploited by the model predictive controller (MPC), enhanced with the stability guarantees by means of Lyapunov constraints. The MPC is solved by making use of a Q-Learning method that, in its online implementation, uses an actor-critic algorithm to approximate the exact solution. Indeed, the Q-learning method provides an accurate and highly efficient solution (in terms of computational time and resources). The proposed approach has been validated through experimental tests, in which a Franka EMIKA panda robot has been used as a test platform. Each user was asked to interact with the robot along the controlled vertical z Cartesian direction. The proposed controller has been compared with a model-based reinforcement learning variable impedance controller (MBRLC) previously developed by some of the authors in order to evaluate the performance. As highlighted in the achieved results, the proposed controller is able to improve the pHRC performance. Additionally, two industrial tasks (a collaborative assembly and a collaborative deposition task) have been demonstrated to prove the applicability of the proposed solution in real industrial scenarios.

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IROS Conference 2022 Conference Paper

Visual Servoing with Geometrically Interpretable Neural Perception

Antonio Paolillo
Mirko Nava
Dario Piga
Alessandro Giusti

An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, allowing an easy application of this technique. This work presents a deep learning approach for solving the perception problem within the visual servoing scheme. An artificial neural network is trained using the supervision coming from the knowledge of the controller and the visual features motion model. In this way, it is possible to give a geometrical interpretation to the estimated visual features, which can be used in the analytical law of the visual servoing. The approach keeps perception and control decoupled, conferring flexibility and interpretability on the whole framework. Simulated and real experiments with a robotic manipulator validate our approach.

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IROS Conference 2021 Conference Paper

Pairwise Preferences-Based Optimization of a Path-Based Velocity Planner in Robotic Sealing Tasks

Loris Roveda
Beatrice Maggioni
Elia Marescotti
Asad Ali Shahid
Andrea M. Zanchettin
Alberto Bemporad
Dario Piga

Production plants are being re-designed to implement human-centered solutions. Especially considering high added-value operations, robots are required to optimize their behavior to achieve a task quality at least comparable to the one obtained by the skilled operators. A manual programming and tuning of the manipulator is not an efficient solution, requiring to adopt towards automated strategies. Adding external sensors (e. g. , cameras) increases the robotic cell complexity and it doesn’t solve the issue since it is usually difficult to build explicit reward functions measuring the robot performance, while it is easier for the user to define a qualitative comparison between two experiments. According to these needs, in this paper, the recently-developed preferences-based optimization approach GLISp is employed and adapted to tune the novel developed path-based velocity planner. The implemented solution defines an intuitive human-centered procedure, capable of transferring (through pairwise preferences between experiments) the task knowledge from the operator to the manipulator. A Franka EMIKA panda robot has been employed as a test platform to perform a robotic sealing task (i. e. , material deposition task), validating the proposed methodology. The proposed approach has been compared with a programming by demonstration approach, and with the manual tuning of the path-based velocity planner. Achieved results demonstrate the improved deposition quality obtained with the proposed optimized path-based velocity planner methodology in a limited number of experimental trials (20).

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ISIPTA Conference 2017 Conference Paper

SOS for Bounded Rationality

Alessio Benavoli
Alessandro Facchini
Dario Piga
Marco Zaffalon

In the gambling foundation of probability theory, rationality requires that a subject should always (never) find desirable all nonnegative (negative) gambles, because no matter the result of the experiment the subject never (always) decreases her money. Evaluating the nonnegativity of a gamble in infinite spaces is a difficult task. In fact, even if we restrict the gambles to be polynomials in $R^n$, the problem of determining nonnegativity is NP-hard. The aim of this paper is to develop a computable theory of desirable gambles. Instead of requiring the subject to accept all nonnegative gambles, we only require her to accept gambles for which she can efficiently determine the nonnegativity (in particular SOS polynomials). We call this new criterion bounded rationality.

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