Lorenzo Natale

Lorenzo Natale
Istituto Italiano di Tecnologia | IIT · Humanoid Sensing and Perception

PhD

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301
Publications
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8,631
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Publications

Publications (301)
Article
There is a growing need for autonomous robots to complete complex tasks robustly in dynamic and unstructured environments. However, current robot performance is limited to simple tasks in controlled environments. To improve robot autonomy in complex environments, the robot's deliberation system must be able to synthesise correct plans for a task an...
Preprint
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Gaze is a crucial social cue in any interacting scenario and drives many mechanisms of social cognition (joint and shared attention, predicting human intention, coordination tasks). Gaze direction is an indication of social and emotional functions affecting the way the emotions are perceived. Evidence shows that embodied humanoid robots endowing so...
Preprint
This study explores using humanoid robots to improve the diagnosis and intervention of Autism Spectrum Disorder (ASD) through objective measures, gaze pattern analysis specifically. Traditional ASD diagnostic tools rely on subjective assessments, while our approach uses the iCub robot to provide quantitative metrics. In our proof-of-concept study,...
Preprint
When humans perform insertion tasks such as inserting a cup into a cupboard, routing a cable, or key insertion, they wiggle the object and observe the process through tactile and proprioceptive feedback. While recent advances in tactile sensors have resulted in tactile-based approaches, there has not been a generalized formulation based on wiggling...
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In this paper, we tackle the problem of estimating 3D contact forces using vision-based tactile sensors. In particular, our goal is to estimate contact forces over a large range (up to 15 N) on any objects while generalizing across different vision-based tactile sensors. Thus, we collected a dataset of over 200K indentations using a robotic arm tha...
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Deep Neural Networks have significantly impacted many computer vision tasks. However, their effectiveness diminishes when test data distribution (target domain) deviates from the one of training data (source domain). In situations where target labels are unavailable and the access to the labeled source domain is restricted due to data privacy or me...
Preprint
In the Vision-and-Language Navigation in Continuous Environments (VLN-CE) task, the human user guides an autonomous agent to reach a target goal via a series of low-level actions following a textual instruction in natural language. However, most existing methods do not address the likely case where users may make mistakes when providing such instru...
Article
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Research in neurophysiology has shown that humans are able to adapt the mechanical stiffness at the hand in order to resist disturbances. This has served as inspiration for optimising stiffness in robot arms during manipulation tasks. Endpoint stiffness is modelled in Cartesian space, as though the hand were in independent rigid body. But an arm is...
Article
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Deep Reinforcement Learning (DRL) has proven effective in learning control policies using robotic grippers, but much less practical for solving the problem of grasping with dexterous hands – especially on real robotic platforms – due to the high dimensionality of the problem. In this work, we focus on the multi-fingered grasping task with the anthr...
Article
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This paper presents and discusses the development and deployment of a tour guide robot as part of the 5 g-TOURS EU research project, aimed at developing applications enabled by 5G technology in different use cases. The objective is the development of an autonomous robotic application where intelligence is off-loaded to a remote machine via 5G netwo...
Article
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In this letter we develop an adaptive tactile force controller for a parallel gripper with low positioning resolution. We show both mathematically and experimentally that a standard integral controller is not suited to control the force in a closed-loop fashion as it induces oscillations in the system. Therefore, we devise an adaptive controller th...
Preprint
This paper explores the role of eye gaze in human-robot interactions and proposes a novel system for detecting objects gazed by the human using solely visual feedback. The system leverages on face detection, human attention prediction, and online object detection, and it allows the robot to perceive and interpret human gaze accurately, paving the w...
Article
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Plankton microorganisms play a huge role in the aquatic food web. Recently, it has been proposed to use plankton as a biosensor, since they can react to even minimal perturbations of the aquatic environment with specific physiological changes, which may lead to alterations in morphology and behavior. Nowadays, the development of high-resolution in-...
Article
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Sense of Agency (SoA) is the feeling of control over one’s actions and their outcomes. A well-established implicit measure of SoA is the temporal interval estimation paradigm, in which participants estimate the time interval between a voluntary action and its sensory consequence. In the present study, we aimed to investigate whether the valence of...
Preprint
Multi-fingered robotic hands could enable robots to perform sophisticated manipulation tasks. However, teaching a robot to grasp objects with an anthropomorphic hand is an arduous problem due to the high dimensionality of state and action spaces. Deep Reinforcement Learning (DRL) offers techniques to design control policies for this kind of problem...
Article
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The problem of passive learning of linear temporal logic formulae consists in finding the best explanation for how two sets of execution traces differ, in the form of the shortest formula that separates the two sets. We approach the problem by implementing an exhaustive search algorithm optimized for execution speed. We apply it to the use-case of...
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Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data. This paper studies how to automatically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment without relying on human intervention, i.e., in a self-supervised fa...
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Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks. However, target domain labels are not accessible in many real-world scenarios. This led to the development of Unsupervised Domain Adaptation (UDA) methods, which only employ unlabeled target samples. Furthermore,...
Preprint
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Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data. This paper studies how to automatically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment without relying on human intervention, i.e., in an utterly self-supe...
Preprint
In this paper, we address the problem of estimating the in-hand 6D pose of an object in contact with multiple vision-based tactile sensors. We reason on the possible spatial configurations of the sensors along the object surface. Specifically, we filter contact hypotheses using geometric reasoning and a Convolutional Neural Network (CNN), trained o...
Preprint
Sense of Agency (SoA) is the feeling of control over one’s actions and their outcomes. A well-established implicit measure of SoA is the temporal interval estimation paradigm, in which participants estimate the time interval between a voluntary action and its sensory consequence. In the present study, we aimed to investigate whether the valence of...
Article
Full-text available
Although robotic grasp planning has been extensively studied in the literature, comparing the performance of different approaches still proves challenging due to the lack of standardization in hardware setup and benchmarking protocols. This work addresses the issue with a threefold contribution. First, it provides a standardized hardware platform a...
Preprint
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Many robotic tasks involving some form of 3D visual perception greatly benefit from a complete knowledge of the working environment. However, robots often have to tackle unstructured environments and their onboard visual sensors can only provide incomplete information due to limited workspaces, clutter or object self-occlusion. In recent years, dee...
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Action recognition is a fundamental capability for humanoid robots to interact and cooperate with humans. This application requires the action recognition system to be designed so that new actions can be easily added, while unknown actions are identified and ignored. In recent years, deep-learning approaches represented the principal solution to th...
Preprint
Performing joint interaction requires constant mutual monitoring of own actions and their effects on the other's behaviour. Such an action-effect monitoring is boosted by social cues and might result in an increasing sense of agency. Joint actions and joint attention are strictly correlated and both of them contribute to the formation of a precise...
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The visual system of a robot has different requirements depending on the application: it may require high accuracy or reliability, be constrained by limited resources or need fast adaptation to dynamically changing environments. In this work, we focus on the instance segmentation task and provide a comprehensive study of different techniques that a...
Chapter
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The current state of the art in cognitive robotics, covering the challenges of building AI-powered intelligent robots inspired by natural cognitive systems. A novel approach to building AI-powered intelligent robots takes inspiration from the way natural cognitive systems—in humans, animals, and biological systems—develop intelligence by exploiting...
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We consider the task of object grasping with a prosthetic hand capable of multiple grasp types. In this setting, communicating the intended grasp type often requires a high user cognitive load which can be reduced adopting shared autonomy frameworks. Among these, so-called eye-in-hand systems automatically control the hand aperture and pre-shaping...
Article
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Social robotics is an emerging field that is expected to grow rapidly in the near future. In fact, it is increasingly more frequent to have robots that operate in close proximity with humans or even collaborate with them in joint tasks. In this context, the investigation of how to endow a humanoid robot with social behavioral skills typical of huma...
Article
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According to the World Health Organization 1, ² the percentage of healthcare dependent population, such as elderly and people with disabilities, among others, will increase over the next years. This trend will put a strain on the health and social systems of most countries. The adoption of robots could assist these health systems in responding to t...
Article
This article addresses the concurrency issues affecting behavior trees (BTs), a popular tool to model the behaviors of autonomous agents in the video game and the robotics industry. BT designers can easily build complex behaviors composing simpler ones, which represents a key advantage of BTs. The parallel composition of BTs expresses a way to comb...
Preprint
6D object pose tracking has been extensively studied in the robotics and computer vision communities. The most promising solutions, leveraging on deep neural networks and/or filtering and optimization, exhibit notable performance on standard benchmarks. However, to our best knowledge, these have not been tested thoroughly against fast object motion...
Preprint
Full-text available
This paper addresses the concurrency issues affecting Behavior Trees (BTs), a popular tool to model the behaviors of autonomous agents in the video game and the robotics industry. BT designers can easily build complex behaviors composing simpler ones, which represents a key advantage of BTs. The parallel composition of BTs expresses a way to combin...
Article
Full-text available
Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar robotic capabilities. This article presents a deep dive into hand-object interaction and human...
Chapter
Accurately detecting objects in unconstrained settings is crucial for robotic agents, such as humanoids, that function in ever-changing environments. Current deep learning based methods achieve remarkable performance on this task on general purpose benchmarks and they are therefore appealing for robotics. However, their high accuracy comes at the p...
Article
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Conventional approaches to robot navigation in unstructured environments rely on information acquired from the LiDAR mounted on the robot base to detect and avoid obstacles. This approach fails to detect obstacles that are too small, or that are invisible because they are outside the LiDAR’s field of view. A possible strategy is to integrate inform...
Preprint
Recent visual pose estimation and tracking solutions provide notable results on popular datasets such as T-LESS and YCB. However, in the real world, we can find ambiguous objects that do not allow exact classification and detection from a single view. In this work, we propose a framework that, given a single view of an object, provides the coordina...
Article
Full-text available
Tactile sensing represents a valuable source of information in robotics for perception of the state of objects and their properties. Modern soft tactile sensors allow perceiving orthogonal forces and, in some cases, relative motions along the surface of the object. Detecting and measuring this kind of lateral motion is fundamental to react to possi...
Preprint
Reliable perception and efficient adaptation to novel conditions are priority skills for humanoids that function in dynamic environments. The vast advancements in latest computer vision research, brought by deep learning methods, are appealing for the robotics community. However, their adoption in applied domains is not straightforward since adapti...
Article
There is a growing interest in Behavior Trees (BTs) as a tool to describe and implement robot behaviors. BTs were devised in the video game industry and their adoption in robotics resulted in the development of ad-hoc libraries to design and execute BTs that fit complex robotics software architectures. While there is broad consensus on how BTs work...
Preprint
In this paper, we present a toolchain to design, execute, and verify robot behaviors. The toolchain follows the guidelines defined by the EU H2020 project RobMoSys and encodes the robot deliberation as a Behavior Tree (BT), a directed tree where the internal nodes model behavior composition and leaf nodes model action or measurement operations. Suc...
Preprint
Full-text available
There is a growing interest in Behavior Trees (BTs) as a tool to describe and implement robot behaviors. BTs were devised in the video game industry and their adoption in robotics resulted in the development of ad-hoc libraries to design and execute BTs that fit complex robotics software architectures. While there is broad consensus on how BTs work...
Preprint
Full-text available
Our research aims to enable automated property verification of deliberative components in robot control architectures. We focus on a formalization of the execution context of Behavior Trees (BTs) to provide a scalable, yet formally grounded, methodology to enable runtime verification and prevent unexpected robot behaviors to hamper deployment. To t...
Preprint
Research on tactile sensing has been progressing at constant pace. In robotics, tactile sensing is typically studied in the context of object grasping and manipulation. In this domain, the development of robust, multi-modal, tactile sensors for robotic hands has supported the study of novel algorithms for in-hand object manipulation, material class...
Preprint
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In this chapter we describe the history and evolution of the iCub humanoid platform. We start by describing the first version as it was designed during the RobotCub EU project and illustrate how it evolved to become the platform that is adopted by more than 30 laboratories world wide. We complete the chapter by illustrating some of the research act...
Preprint
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Hand-eye calibration of laser profile sensors is the process of extracting the homogeneous transformation between the laser profile sensor frame and the end-effector frame of a robot in order to express the data extracted by the sensor in the robot's global coordinate system. For laser profile scanners this is a challenging procedure, as they provi...
Article
Full-text available
Tracking the 6D pose and velocity of objects represents a fundamental requirement for modern robotics manipulation tasks. This paper proposes a 6D object pose tracking algorithm, called MaskUKF, that combines deep object segmentation networks and depth information with a serial Unscented Kalman Filter to track the pose and the velocity of an object...
Article
6D object pose tracking has been extensively studied in the robotics and computer vision communities. The most promising solutions, leveraging on deep neural networks and/or filtering and optimization, exhibit notable performance on standard benchmarks. However, to our best knowledge, these have not been tested thoroughly against fast object motion...
Preprint
Several object detection methods have recently been proposed in the literature, the vast majority based on Deep Convolutional Neural Networks (DCNNs). Such architectures have been shown to achieve remarkable performance, at the cost of computationally expensive batch training and extensive labeling. These methods have important limitations for robo...
Conference Paper
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5G mobile networks are designed to fulfill very stringent requirements and support new vertical use cases. This transition to a vertical oriented delivery model will have a strong impact in the touristic sector. In this context, the "touristic city node" of 5G-TOURS, built in the city of Turin, aims at exploiting the potential of the media vertical...
Preprint
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Object detection is a fundamental task for robots to operate in unstructured environments. Today, there are several deep learning algorithms that solve this task with remarkable performance. Unfortunately, training such systems requires several hours of GPU time. For robots, to successfully adapt to changes in the environment or learning new object...
Preprint
Object segmentation is a key component in the visual system of a robot that performs tasks like grasping and object manipulation, especially in presence of occlusions. Like many other Computer Vision tasks, the adoption of deep architectures has made available algorithms that perform this task with remarkable performance. However, adoption of such...
Conference Paper
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The paper introduce a robotics software control architecture suitable for the development of complete robotic industrial applications. The architecture fuse the state-of-the-art software technologies in a single standalone platform to provide an easy integration between all the software components necessary to control a robotic application, i.e. PL...
Preprint
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In this paper, we propose Belief Behavior Trees (BBTs), an extension to Behavior Trees (BTs) that allows to automatically create a policy that controls a robot in partially observable environments. We extend the semantic of BTs to account for the uncertainty that affects both the conditions and action nodes of the BT. The tree gets synthesized foll...
Preprint
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Task planning in a probabilistic belief state domains allows generating complex and robust execution policies in those domains affected by state uncertainty. The performance of a task planner relies on the belief state representation. However, current belief state representation becomes easily intractable as the number of variables and execution ti...