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Alessandro Giusti

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35 papers
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35

EAAI Journal 2026 Journal Article

Blinking like Fireflies: Convolutional neural networks for bio-inspired visible light communication between nano-drones

  • Luca Crupi
  • Nicholas Carlotti
  • Alessandro Giusti
  • Daniele Palossi

We present a novel visible light communication (VLC) system to enable swarms of pocket-sized nano-drones to exchange messages through light-emitting diodes’ (LEDs) blinking, like fireflies. While a nano-drone is sending a message encoded via LED’s blinking, a receiver one reconstructs it employing only a low-resolution camera and an ultra-low-power GreenWaves application processor 8 (GAP8) system-on-chip running a compact (7500parameters) fully convolutional neural network (FCNN) that achieves 0. 87 area under the curve (improving upon prior nano-drone VLC work by +0. 27) and predicts both the LEDs’ state and the image position of the sender nano-drone. A stream of LEDs’ state (on/off) is then continuously fed to a synchronization-free decoder, which also runs aboard the nano-drone. Our approach, only leveraging inexpensive onboard hardware (camera and LEDs), achieves competitive accuracy compared to state-of-the-art VLC methods designed for larger drones while consuming orders of magnitude less power (101milliwatt compared to more than 25watt). By employing a pair of Crazyflie nano-drones, our FCNN reaches 39frames per second, which allows from 2. 8 to 8. 6bits per second throughput with a per-bit accuracy of 93 percent and from 0. 6 to 1. 6bits per second with a per-bit accuracy of 99. 8 percent. Finally, our closed-loop system is experimentally demonstrated in the field, where two fully autonomous nano-drones exchange messages with our VLC technique while following each other thanks to the predicted image position.

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IROS Conference 2024 Conference Paper

A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction

  • Simone Arreghini
  • Gabriele Abbate
  • Alessandro Giusti
  • Antonio Paolillo

We consider a service robot that offers chocolate treats to people passing in its proximity: it has the capability of predicting in advance a person’s intention to interact, and to actuate an "offering" gesture, subtly extending the tray of chocolates towards a given target. We run the system for more than 5 hours across 3 days and two different crowded public locations; the system implements three possible behaviors that are randomly toggled every few minutes: passive (e. g. never performing the offering gesture); or active, triggered by either a naive distance-based rule, or a smart approach that relies on various behavioral cues of the user. We collect a real-world dataset that includes information on 1777 users with several spontaneous human-robot interactions and study the influence of robot actions on people’s behavior. Our comprehensive analysis suggests that users are more prone to engage with the robot when it proactively starts the interaction. We release the dataset and provide insights to make our work reproducible for the community. Also, we report qualitative observations collected during the acquisition campaign and identify future challenges and research directions in the domain of social human-robot interaction.

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ICRA Conference 2024 Conference Paper

High-throughput Visual Nano-drone to Nano-drone Relative Localization using Onboard Fully Convolutional Networks

  • Luca Crupi
  • Alessandro Giusti
  • Daniele Palossi

Relative drone-to-drone localization is a fundamental building block for any swarm operations. We address this task in the context of miniaturized nano-drones, i. e. , ∼10cm in diameter, which show an ever-growing interest due to novel use cases enabled by their reduced form factor. The price for their versatility comes with limited onboard resources, i. e. , sensors, processing units, and memory, which limits the complexity of the onboard algorithms. A traditional solution to overcome these limitations is represented by lightweight deep learning models directly deployed aboard nano-drones. This work tackles the challenging relative pose estimation between nano-drones using only a gray-scale low-resolution camera and an ultra-low-power System-on-Chip (SoC) hosted onboard. We present a vertically integrated system based on a novel vision-based fully convolutional neural network (FCNN), which runs at 39Hz within 101mW onboard a Crazyflie nano-drone extended with the GWT GAP8 SoC. We compare our FCNN against three State-of-the-Art (SoA) systems. Considering the best-performing SoA approach, our model results in a R 2 improvement from 32 to 47% on the horizontal image coordinate and from 18 to 55% on the vertical image coordinate, on a real-world dataset of ∼30k images. Finally, our in-field tests show a reduction of the average tracking error of 37% compared to a previous SoA work and an endurance performance up to the entire battery lifetime of 4min.

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IROS Conference 2024 Conference Paper

Learning to Estimate the Pose of a Peer Robot in a Camera Image by Predicting the States of its LEDs

  • Nicholas Carlotti
  • Mirko Nava
  • Alessandro Giusti

We consider the problem of training a fully convolutional network to estimate the relative 6D pose of a robot given a camera image, when the robot is equipped with independent controllable LEDs placed in different parts of its body. The training data is composed by few (or zero) images labeled with a ground truth relative pose and many images labeled only with the true state (ON or OFF) of each of the peer LEDs. The former data is expensive to acquire, requiring external infrastructure for tracking the two robots; the latter is cheap as it can be acquired by two unsupervised robots moving randomly and toggling their LEDs while sharing the true LED states via radio. Training with the latter dataset on estimating the LEDs’ state of the peer robot (pretext task) promotes learning the relative localization task (end task). Experiments on real-world data acquired by two autonomous wheeled robots show that a model trained only on the pretext task successfully learns to localize a peer robot on the image plane; fine-tuning such model on the end task with few labeled images yields statistically significant improvements in 6D relative pose estimation with respect to baselines that do not use pretext-task pre-training, and alternative approaches. Estimating the state of multiple independent LEDs promotes learning to estimate relative heading. The approach works even when a large fraction of training images do not include the peer robot and generalizes well to unseen environments.

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ICRA Conference 2024 Conference Paper

On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors

  • Elia Cereda
  • Manuele Rusci
  • Alessandro Giusti
  • Daniele Palossi

Sub-50g nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite severe hardware constraints (i. e. , sub-100mW processor). When deployed in unknown environments not represented in the training data, these models often underperform due to domain shift. To cope with this fundamental problem, we propose, for the first time, on-device learning aboard nano-drones, where the first part of the in-field mission is dedicated to self-supervised finetuning of a pre-trained convolutional neural network (CNN). Leveraging a real-world vision-based regression task, we thoroughly explore performance-cost trade-offs of the fine-tuning phase along three axes: i) dataset size (more data increases the regression performance but requires more memory and longer computation); ii) methodologies (e. g. , fine-tuning all model parameters vs. only a subset); and iii) self-supervision strategy. Our approach demonstrates an improvement in mean absolute error up to 30% compared to the pre-trained baseline, requiring only 22s fine-tuning on an ultra-low-power GWT GAP9 System-on-Chip. Addressing the domain shift problem via on-device learning aboard nano-drones not only marks a novel result for hardware-limited robots but lays the ground for more general advancements for the entire robotics community.

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ICRA Conference 2024 Conference Paper

Predicting the Intention to Interact with a Service Robot: the Role of Gaze Cues

  • Simone Arreghini
  • Gabriele Abbate
  • Alessandro Giusti
  • Antonio Paolillo

For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact, which can be trained in a self-supervised way. Our main contribution is a study of the benefit of features representing the person’s gaze in this context. Extensive experiments on a novel dataset show that the inclusion of gaze cues significantly improves the classifier performance (AUROC increases from 84. 5 % to 91. 2 %); the distance at which an accurate classification can be achieved improves from 2. 4 m to 3. 2 m. We also quantify the system’s ability to adapt to new environments without external supervision. Qualitative experiments show practical applications with a waiter robot.

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IROS Conference 2024 Conference Paper

Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression

  • Nicky Zimmerman
  • Alessandro Giusti
  • Jérôme Guzzi

Global localization is essential in enabling robot autonomy, and collaborative localization is key for multi-robot systems, allowing for more efficient planning and execution of tasks. In this paper, we address the task of collaborative global localization under computational and communication constraints. We propose a method which reduces the amount of information exchanged and the computational cost. We also analyze, implement and open-source seminal approaches, which we believe to be a valuable contribution to the community. We exploit techniques for distribution compression in near-linear time, with error guarantees. We evaluate our approach and the implemented baselines on multiple challenging scenarios, simulated and real-world. Our approach can run online on an onboard computer. We release an open-source C++/ROS2 implementation of our approach, as well as the baselines. 1

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ICRA Conference 2023 Conference Paper

Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs

  • Elia Cereda
  • Luca Crupi
  • Matteo Risso
  • Alessio Burrello
  • Luca Benini
  • Alessandro Giusti
  • Daniele Jahier Pagliari
  • Daniele Palossi

Miniaturized autonomous unmanned aerial vehicles (UAVs) are an emerging and trending topic. With their form factor as big as the palm of one hand, they can reach spots otherwise inaccessible to bigger robots and safely operate in human surroundings. The simple electronics aboard such robots (sub-100 mW) make them particularly cheap and attractive but pose significant challenges in enabling onboard sophisticated intelligence. In this work, we leverage a novel neural architecture search (NAS) technique to automatically identify several Pareto-optimal convolutional neural networks (CNNs) for a visual pose estimation task. Our work demonstrates how reallife and field-tested robotics applications can concretely leverage NAS technologies to automatically and efficiently optimize CNNs for the specific hardware constraints of small UAVs. We deploy several NAS-optimized CNNs and run them in closed-loop aboard a 27-g Crazyflie nano-UAV equipped with a parallel ultra-low power System-on-Chip. Our results improve the State-of-the-Art by reducing the in-field control error of 32% while achieving a real-time onboard inference-rate of ~10Hz@10mW and ~50Hz@90mW.

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IROS Conference 2023 Conference Paper

Sim-to-Real Vision-Depth Fusion CNNs for Robust Pose Estimation Aboard Autonomous Nano-quadcopters

  • Luca Crupi
  • Elia Cereda
  • Alessandro Giusti
  • Daniele Palossi

Nano-quadcopters are versatile platforms attracting the interest of both academia and industry. Their tiny form factor, i. e. , ~ 10 cm diameter, makes them particularly useful in narrow scenarios and harmless in human proximity. However, these advantages come at the price of ultra-constrained onboard computational and sensorial resources for autonomous operations. This work addresses the task of estimating human pose aboard nano-drones by fusing depth and images in a novel CNN exclusively trained in simulation yet capable of robust predictions in the real world. We extend a commercial off-the-shelf (COTS) Crazyflie nano-drone - equipped with a 320x240 px camera and an ultra-low-power System-on-Chip - with a novel multi-zone (8 x 8) depth sensor. We design and compare different deep-learning models that fuse depth and image inputs. Our models are trained exclusively on simulated data for both inputs, and transfer well to the real world: field testing shows an improvement of 58% and 51 % of our depth+camera system w. r. t. a camera-only State-of-the-Art baseline on the horizontal and angular mean pose errors, respectively. Our prototype is based on COTS components, which facilitates reproducibility and adoption of this novel class of systems.

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ICRA Conference 2023 Conference Paper

Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors

  • Stefano Bonato
  • Stefano Carlo Lambertenghi
  • Elia Cereda
  • Alessandro Giusti
  • Daniele Palossi

Precise relative localization is a crucial functional block for swarm robotics. This work presents a novel au-tonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones, i. e. , sub-40g of weight and sub-100mW processing power. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, from the dataset collection to the final in-field deployment, including dataset augmentation, quantization, and system op-timizations. Experimental results show that our DNN can precisely localize a 10 cm-size target nano-drone by employing only low-resolution monochrome images, up to ~2m distance. On a disjoint testing dataset our model yields a mean R 2 score of 0. 42 and a root mean square error of 18 cm, which results in a mean in-field prediction error of 15 cm and in a closed-loop control error of 17 cm, over a ~60 s-flight test. Ultimately, the proposed system improves the State-of-the-Art by showing long-endurance tracking performance (up to 2 min continuous tracking), generalization capabilities being deployed in a never-seen-before environment, and requiring a minimal power consumption of 95 mW for an onboard real-time inference-rate of 48 Hz.

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AAAI Conference 2022 Short Paper

A Deep Learning-Based Face Mask Detector for Autonomous Nano-Drones (Student Abstract)

  • Eiman AlNuaimi
  • Elia Cereda
  • Rafail Psiakis
  • Suresh Sugumar
  • Alessandro Giusti
  • Daniele Palossi

We present a deep neural network (DNN) for visually classifying whether a person is wearing a protective face mask or not. Our DNN is compatible with a resource-limited, sub- 10-cm nano-drone: this robotic platform is an ideal candidate to fly in human proximity and safely perform ubiquitous visual perception tasks. This paper describes our pipeline, including: dataset collection; selection and training of a fullprecision (i. e. , float32) DNN; network quantization to int8 precision, enabling the DNN’s deployment on a parallel ultralow power (PULP) system-on-chip aboard a nano-drone. Results demonstrate the efficacy of our pipeline with a mean area under the ROC curve score of 0. 81, which drops by only ∼2% when quantized to 8-bit for deployment.

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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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ICRA Conference 2021 Conference Paper

Pointing at Moving Robots: Detecting Events from Wrist IMU Data

  • Gabriele Abbate
  • Boris Gromov
  • Luca Maria Gambardella
  • Alessandro Giusti

We propose a practical approach for detecting the event that a human wearing an IMU-equipped bracelet points at a moving robot; the approach uses a learned classifier to verify if the robot motion (as measured by its odometry) matches the wrist motion, and does not require that the relative pose of the operator and robot is known in advance. To train the model and validate the system, we collect datasets containing hundreds of real-world pointing events. Extensive experiments quantify the performance of the classifiers and relevant metrics of the resulting detectors; the approach is implemented in a real-world demonstrator that allows users to land quadrotors by pointing at them.

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ICRA Conference 2020 Conference Paper

Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures

  • Boris Gromov
  • Jérôme Guzzi
  • Luca Maria Gambardella
  • Alessandro Giusti

We present an approach for controlling the position of a quadrotor in 3D space using pointing gestures; the task is difficult because it is in general ambiguous to infer where, along the pointing ray, the robot should go. We propose and validate a pragmatic solution based on a push button acting as a simple additional input device which switches between different virtual workspace surfaces. Results of a study involving ten subjects show that the approach performs well on a challenging 3D piloting task, where it compares favorably with joystick control.

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ICRA Conference 2019 Conference Paper

On the Impact of Uncertainty for Path Planning

  • Jérôme Guzzi
  • Ricardo Omar Chávez García
  • Luca Maria Gambardella
  • Alessandro Giusti

We consider the problem of planning paths on graphs with some edges whose traversability is uncertain; for each uncertain edge, we are given a probability of being traversable (e. g. , by a learned classifier). We categorize different interpretations of the problem that are meaningful for mobile robots navigating partially-known environments, each of which yields a different formalization; we then focus on the case in which the true traversability of an edge is revealed only when the agent visits one of its endpoints (Canadian Traveller Problem). In this context, we design a large simulation campaign on synthetic and real-world maps to study the impact of two different factors: the planning strategy, and the amount of uncertainty (which could depend on the quality of the classifier producing traversability estimates).

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ICRA Conference 2019 Conference Paper

Proximity Human-Robot Interaction Using Pointing Gestures and a Wrist-mounted IMU

  • Boris Gromov
  • Gabriele Abbate
  • Luca Maria Gambardella
  • Alessandro Giusti

We present a system for interaction between co-located humans and mobile robots, which uses pointing gestures sensed by a wrist-mounted IMU. The operator begins by pointing, for a short time, at a moving robot. The system thus simultaneously determines: that the operator wants to interact; the robot they want to interact with; and the relative pose among the two. Then, the system can reconstruct pointed locations in the robot's own reference frame, and provide real-time feedback about them so that the user can adapt to misalignments. We discuss the challenges to be solved to implement such a system and propose practical solutions, including variants for fast flying robots and slow ground robots. We report different experiments with real robots and untrained users, validating the individual components and the system as a whole.

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AAAI Conference 2019 System Paper

Realtime Generation of Audible Textures Inspired by a Video Stream

  • Simone Mellace
  • Jérôme Guzzi
  • Alessandro Giusti
  • Luca M. Gambardella

We showcase a model to generate a soundscape from a camera stream in real time. The approach relies on a training video with an associated meaningful audio track; a granular synthesizer generates a novel sound by randomly sampling and mixing audio data from such video, favoring timestamps whose frame is similar to the current camera frame; the semantic similarity between frames is computed by a pretrained neural network. The demo is interactive: a user points a mobile phone to different objects and hears how the generated sound changes.

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ICRA Conference 2019 Conference Paper

Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches

  • Dario Mantegazza
  • Jérôme Guzzi
  • Luca Maria Gambardella
  • Alessandro Giusti

We consider the task of controlling a quadrotor to hover in front of a freely moving user, using input data from an onboard camera. On this specific task we compare two widespread learning paradigms: a mediated approach, which learns a high-level state from the input and then uses it for deriving control signals; and an end-to-end approach, which skips high-level state estimation altogether. We show that despite their fundamental difference, both approaches yield equivalent performance on this task. We finally qualitatively analyze the behavior of a quadrotor implementing such approaches.

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AAMAS Conference 2018 Conference Paper

Artificial Emotions as Dynamic Modulators of Individual and Group Behavior in Multi-robot System

  • J�r�me Guzzi
  • Alessandro Giusti
  • Luca M. Gambardella
  • Gianni A. Di Caro

We propose a model for adaptation and implicit coordination in multi-robot systems based on the definition of artificial emotions, which play two main roles: modulators of individual robot behavior, and means of communication among different robots for systemlevel social coordination. We model emotions as compressed representations of a robot’s internal state that are subject to a dynamics influenced by internal and external conditions; they can be efficiently exposed to nearby robots, allowing to achieve local, group-level behavior adaptation and modulation, with minimal computational and bandwidth requirements.

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AAAI Conference 2018 Conference Paper

Introducing Machine Learning Concepts by Training a Neural Network to Recognize Hand Gestures

  • Alessandro Giusti
  • David Huber
  • Luca Gambardella

We present an interactive guided activity to introduce supervised learning by training a deep neural network (treated as a black box) to recognize “rock paper scissors” hand gestures from unconstrained images. The audience is actively involved in acquiring a varied and representative dataset, on which the rest of the activity is based. Covered concepts include the training/evaluation split, classifier evaluation, baseline accuracy, overfitting, generalization, data augmentation.

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AAAI Conference 2018 System Paper

Learning an Image-based Obstacle Detector With Automatic Acquisition of Training Data

  • Stefano Toniolo
  • Jérôme Guzzi
  • Luca Gambardella
  • Alessandro Giusti

We detect and localize obstacles in front of a mobile robot by means of a deep neural network that maps images acquired from a forward-looking camera to the outputs of five proximity sensors. The robot autonomously acquires training data in multiple environments; once trained, the network can detect obstacles and their position also in unseen scenarios, and can be used on different robots, not equipped with proximity sensors. We demonstrate both the training and deployment phases on a small modified Thymio robot.

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AAAI Conference 2018 Short Paper

Learning to Detect Pointing Gestures From Wearable IMUs

  • Denis Broggini
  • Boris Gromov
  • Alessandro Giusti
  • Luca Gambardella

We propose a learning-based system for detecting when a user performs a pointing gesture, using data acquired from IMU sensors, by means of a 1D convolutional neural network. We quantitatively evaluate the resulting detection accuracy, and discuss an application to a human-robot interaction task where pointing gestures are used to guide a quadrotor landing.

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AAAI Conference 2018 Conference Paper

Mighty Thymio for University-Level Educational Robotics

  • Jérôme Guzzi
  • Alessandro Giusti
  • Gianni Di Caro
  • Luca Gambardella

Thymio is a small, inexpensive, mass-produced mobile robot with widespread use in primary and secondary education. In order to make it more versatile and effectively use it in later educational stages, including university levels, we have expanded Thymio’s capabilities by adding off-the-shelf hardware and open software components. The resulting robot, that we call Mighty Thymio, provides additional sensing functionalities, increased computing power, networking, and full ROS integration. We present the architecture of Mighty Thymio and show its application in advanced educational activities.

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IROS Conference 2018 Conference Paper

Robot Identification and Localization with Pointing Gestures

  • Boris Gromov
  • Luca Maria Gambardella
  • Alessandro Giusti

We propose a novel approach to establish the relative pose of a mobile robot with respect to an operator that wants to interact with it; we focus on scenarios in which the robot is in the same environment as the operator, and is visible to them. The approach is based on comparing the trajectory of the robot, which is known in the robot's odometry frame, to the motion of the arm of the operator, who, for a short time, keeps pointing at the robot they want to interact with. In multi-robot scenarios, the same approach can be used to simultaneously identify which robot the operator wants to interact with. The main advantage over alternatives is that our system only relies on the robot's odometry, on a wearable inertial measurement unit (IMU), and, crucially, on the operator's own perception. We experimentally show the feasibility of our approach using real-world robots.

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ICRA Conference 2015 Conference Paper

Fair Multi-Target Tracking in Cooperative Multi-Robot systems

  • Jacopo Banfi
  • Jérôme Guzzi
  • Alessandro Giusti
  • Luca Maria Gambardella
  • Gianni A. Di Caro

Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) denotes a class of problems in which a set of autonomous mobile robots equipped with limited-range sensors are used to keep under observation a (possibly larger) set of mobile targets. Robots cooperatively plan their motion in order to maximize the time during which each target lies within the sensing range of at least one robot.

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IROS Conference 2014 Conference Paper

Human-swarm interaction using spatial gestures

  • Jawad Nagi
  • Alessandro Giusti
  • Luca Maria Gambardella
  • Gianni A. Di Caro

This paper presents a machine vision based approach for human operators to select individual and groups of autonomous robots from a swarm of UAVs. The angular distance between the robots and the human is estimated using measures of the detected human face, which aids to determine human and multi-UAV localization and positioning. In turn, this is exploited to effectively and naturally make the human select the spatially situated robots. Spatial gestures for selecting robots are presented by the human operator using tangible input devices (i. e. , colored gloves). To select individuals and groups of robot we formulate a vocabulary of two-handed spatial pointing gestures. With the use of a Support Vector Machine (SVM) trained in a cascaded multi-binary-class configuration, the spatial gestures are effectively learned and recognized by a swarm of UAVs.

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IROS Conference 2014 Conference Paper

Interactive Augmented Reality for understanding and analyzing multi-robot systems

  • Fabrizio Ghiringhelli
  • Jérôme Guzzi
  • Gianni A. Di Caro
  • Vincenzo Caglioti
  • Luca Maria Gambardella
  • Alessandro Giusti

Once a multi-robot system is implemented on real hardware and tested in the real world, analyzing its evolution and debugging unexpected behaviors is often a very difficult task. We present a tool for aiding this activity, by visualizing an Augmented Reality overlay on a live video feed acquired by a fixed camera overlooking the robot environment. Such overlay displays live information exposed by each robot, which may be textual (state messages), symbolic (graphs, charts), or, most importantly, spatially-situated; spatially-situated information is related to the environment surrounding the robot itself, such as for example the perceived position of neighboring robots, the perceived extent of obstacles, the path the robot plans to follow. We show that, by directly representing such information on the environment it refers to, our proposal removes a layer of indirection and significantly eases the process of understanding complex multi-robot systems. We describe how the system is implemented, discuss application examples in different scenarios, and provide supplementary material including demonstration videos and a functional implementation.

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IROS Conference 2014 Conference Paper

Kinect-based people detection and tracking from small-footprint ground robots

  • Armando Pesenti Gritti
  • Oscar Tarabini
  • Jérôme Guzzi
  • Gianni A. Di Caro
  • Vincenzo Caglioti
  • Luca Maria Gambardella
  • Alessandro Giusti

Small-footprint mobile ground robots, such as the popular Turtlebot and Kobuki platforms, are by necessity equipped with sensors which lie close to the ground. Reliably detecting and tracking people from this viewpoint is a challenging problem, whose solution is a key requirement for many applications involving sharing of common spaces and close human-robot interaction. We present a robust solution for cluttered indoor environments, using an inexpensive RGB-D sensor such as the Microsoft Kinect or Asus Xtion. Even in challenging scenarios with multiple people in view at once and occluding each other, our system solves the person detection problem significantly better than alternative approaches, reaching a precision, recall and F1-score of 0. 85, 0. 81 and 0. 83, respectively. Evaluation datasets, a real-time ROS-enabled implementation and demonstration videos are provided as supplementary material.

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ICRA Conference 2014 Conference Paper

Online feature extraction for the incremental learning of gestures in human-swarm interaction

  • Jawad Nagi
  • Alessandro Giusti
  • Farrukh Nagi
  • Luca Maria Gambardella
  • Gianni A. Di Caro

We present a novel approach for the online learning of hand gestures in swarm robotic (multi-robot) systems. We address the problem of online feature learning by proposing Convolutional Max-Pooling (CMP), a simple feed-forward two-layer network derived from the deep hierarchical Max-Pooling Convolutional Neural Network (MPCNN). To learn and classify gestures in an online and incremental fashion, we employ a 2nd order online learning method, namely the Soft-Confidence Weighted (SCW) learning scheme. In order for all robots to collectively take part in the learning and recognition task and obtain a swarm-level classification, we build a distributed consensus by fusing the individual decision opinions of robots together with the individual weights generated from multiple classifiers. Accuracy, robustness, and scalability of obtained solutions have been verified through emulation experiments performed on a large data set of real data acquired by a networked swarm of robots.

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ICRA Conference 2013 Conference Paper

Human-friendly robot navigation in dynamic environments

  • Jérôme Guzzi
  • Alessandro Giusti
  • Luca Maria Gambardella
  • Guy Theraulaz
  • Gianni A. Di Caro

The vision-based mechanisms that pedestrians in social groups use to navigate in dynamic environments, avoiding obstacles and each others, have been subject to a large amount of research in social anthropology and biological sciences. We build on recent results in these fields to develop a novel fully-distributed algorithm for robot local navigation, which implements the same heuristics for mutual avoidance adopted by humans. The resulting trajectories are human-friendly, because they can intuitively be predicted and interpreted by humans, making the algorithm suitable for the use on robots sharing navigation spaces with humans. The algorithm is computationally light and simple to implement. We study its efficiency and safety in presence of sensing uncertainty, and demonstrate its implementation on real robots. Through extensive quantitative simulations we explore various parameters of the system and demonstrate its good properties in scenarios of different complexity. When the algorithm is implemented on robot swarms, we could observe emergent collective behaviors similar to those observed in human crowds.

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IROS Conference 2013 Conference Paper

Local reactive robot navigation: A comparison between reciprocal velocity obstacle variants and human-like behavior

  • Jérôme Guzzi
  • Alessandro Giusti
  • Luca Maria Gambardella
  • Gianni A. Di Caro

Most local robot navigation algorithms are based on the concept of velocity obstacle, a mechanistic approach to the navigation problem in which a solution is engineered from scratch. Over the years, a number of different velocity obstacle variants have been developed to effectively handle multi-robot systems. In parallel, an alternative, human-inspired approach for robot navigation has been recently proposed, which derives from the observation and modeling of crowds of pedestrians. We discuss similarities and differences among two broadly used obstacle-velocity variants, namely Hybrid Reciprocal Velocity Obstacle and Optimal Reciprocal Collision Avoidance, and the human-inspired approach. How do these differences (which are often subtle) impact performance, and why? We answer these questions through extensive simulation experiments, wherein we evaluate the the algorithms for safety, trajectory efficiency, and emergence of collective behaviors, in different challenging multi-robot scenarios using both ideal and realistic models for robots and sensing.

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IROS Conference 2012 Conference Paper

Cooperative sensing and recognition by a swarm of mobile robots

  • Alessandro Giusti
  • Jawad Nagi
  • Luca Maria Gambardella
  • Gianni A. Di Caro

We present an approach for distributed real-time recognition tasks using a swarm of mobile robots. We focus on the visual recognition of hand gestures, but the solutions that we provide have general applicability and address a number of challenges common to many distributed sensing and classification problems. In our approach, robots acquire and process hand images from multiple points of view, most of which do not allow for a satisfactory classification. Each robot is equipped with a statistical classifier, which is used to generate an opinion for the sensed gesture. Using a low-bandwidth wireless channel, the robots locally exchange their opinions. They also exploit mobility to adapt their positions to maximize the mutual information collectively gathered by the swarm. A distributed consensus protocol is implemented, to allow to rapidly settle on a decision once enough evidence is available. The system is implemented and demonstrated on real robots. In addition, extensive quantitative results of emulation experiments, based on a real image dataset, are reported. We consider different scenarios and study the scalability and the robustness of the swarm performance for distributed recognition.

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NeurIPS Conference 2012 Conference Paper

Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images

  • Dan Ciresan
  • Alessandro Giusti
  • Luca Gambardella
  • Jürgen Schmidhuber

We address a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy (EM) images. This is necessary to efficiently map 3D brain structure and connectivity. To segment {\em biological} neuron membranes, we use a special type of deep {\em artificial} neural network as a pixel classifier. The label of each pixel (membrane or non-membrane) is predicted from raw pixel values in a square window centered on it. The input layer maps each window pixel to a neuron. It is followed by a succession of convolutional and max-pooling layers which preserve 2D information and extract features with increasing levels of abstraction. The output layer produces a calibrated probability for each class. The classifier is trained by plain gradient descent on a $512 \times 512 \times 30$ stack with known ground truth, and tested on a stack of the same size (ground truth unknown to the authors) by the organizers of the ISBI 2012 EM Segmentation Challenge. Even without problem-specific post-processing, our approach outperforms competing techniques by a large margin in all three considered metrics, i. e. \emph{rand error}, \emph{warping error} and \emph{pixel error}. For pixel error, our approach is the only one outperforming a second human observer.

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AAMAS Conference 2012 Conference Paper

Distributed Consensus for Interaction between Humans and Mobile Robot Swarms

  • Alessandro Giusti
  • Jawad Nagi
  • Luca Gambardella
  • Gianni Di Caro

The purpose of the demonstrator is to present a novel system for gesture-based interaction between humans and a swarm of mobile robots. The human interacts with the swarm by showing hand gestures using an orange glove. Following initial hand glove detection, the robots move to adapt their positions and viewpoints. The purpose is to improve individual sensing performance and maximize the gesture information mutually gathered by the swarm as a whole. Using multi-hop message relaying, robots spread their opinions and the associated confidence about the issued hand gesture throughout the swarm. To let the robots in the swarm integrate and weight the different opinions, we developed a distributed consensus protocol. When a robot has gathered enough evidence, it takes a decision for the hand gesture, and sends it into the swarm. Different decisions compete with each other. The one assessed with the highest confidence eventually wins. When consensus is reached about the hand gesture, the swarm acts accordingly, for example by moving to a location, or splitting into groups. The working of the system is shown and explained in the video accessible at the following address: {\footnotesize \url{http: //www. idsia. ch/~gianni/SwarmRobotics/aamasdemo. zip}}

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