634 research outputs found
Teleoperation Methods for High-Risk, High-Latency Environments
In-Space Servicing, Assembly, and Manufacturing (ISAM) can enable larger-scale and longer-lived infrastructure projects in space, with interest ranging from commercial entities to the US government. Servicing, in particular, has the potential to vastly increase the usable lifetimes of satellites. However, the vast majority of spacecraft on low Earth orbit today were not designed to be serviced on-orbit. As such, several of the manipulations during servicing cannot easily be automated and instead require ground-based teleoperation.
Ground-based teleoperation of on-orbit robots brings its own challenges of high latency communications, with telemetry delays of several seconds, and difficulties in visualizing the remote environment due to limited camera views. We explore teleoperation methods to alleviate these difficulties, increase task success, and reduce operator load.
First, we investigate a model-based teleoperation interface intended to provide the benefits of direct teleoperation even in the presence of time delay. We evaluate the model-based teleoperation method using professional robot operators, then use feedback from that study to inform the design of a visual planning tool for this task, Interactive Planning and Supervised Execution (IPSE). We describe and evaluate the IPSE system and two interfaces, one 2D using a traditional mouse and keyboard and one 3D using an Intuitive Surgical da Vinci master console. We then describe and evaluate an alternative 3D interface using a Meta Quest head-mounted display. Finally, we describe an extension of IPSE to allow human-in-the-loop planning for a redundant robot. Overall, we find that IPSE improves task success rate and decreases operator workload compared to a conventional teleoperation interface
Une méthode de mesure du mouvement humain pour la programmation par démonstration
Programming by demonstration (PbD) is an intuitive approach to impart a task to a robot from one or several demonstrations by the human teacher. The acquisition of the demonstrations involves the solution of the correspondence problem when the teacher and the learner differ in sensing and actuation. Kinesthetic guidance is widely used to perform demonstrations. With such a method, the robot is manipulated by the teacher and the demonstrations are recorded by the robot's encoders. In this way, the correspondence problem is trivial but the teacher dexterity is afflicted which may impact the PbD process. Methods that are more practical for the teacher usually require the identification of some mappings to solve the correspondence problem. The demonstration acquisition method is based on a compromise between the difficulty of identifying these mappings, the level of accuracy of the recorded elements and the user-friendliness and convenience for the teacher. This thesis proposes an inertial human motion tracking method based on inertial measurement units (IMUs) for PbD for pick-and-place tasks. Compared to kinesthetic guidance, IMUs are convenient and easy to use but can present a limited accuracy. Their potential for PbD applications is investigated.
To estimate the trajectory of the teacher's hand, 3 IMUs are placed on her/his arm segments (arm, forearm and hand) to estimate their orientations. A specific method is proposed to partially compensate the well-known drift of the sensor orientation estimation around the gravity direction by exploiting the particular configuration of the demonstration. This method, called heading reset, is based on the assumption that the sensor passes through its original heading with stationary phases several times during the demonstration. The heading reset is implemented in an integration and vector observation algorithm. Several experiments illustrate the advantages of this heading reset.
A comprehensive inertial human hand motion tracking (IHMT) method for PbD is then developed. It includes an initialization procedure to estimate the orientation of each sensor with respect to the human arm segment and the initial orientation of the sensor with respect to the teacher attached frame. The procedure involves a rotation and a static position of the extended arm. The measurement system is thus robust with respect to the positioning of the sensors on the segments. A procedure for estimating the position of the human teacher relative to the robot and a calibration procedure for the parameters of the method are also proposed. At the end, the error of the human hand trajectory is measured experimentally and is found in an interval between mm and mm. The mappings to solve the correspondence problem are identified. Unfortunately, the observed level of accuracy of this IHMT method is not sufficient for a PbD process.
In order to reach the necessary level of accuracy, a method is proposed to correct the hand trajectory obtained by IHMT using vision data. A vision system presents a certain complementarity with inertial sensors. For the sake of simplicity and robustness, the vision system only tracks the objects but not the teacher. The correction is based on so-called Positions Of Interest (POIs) and involves 3 steps: the identification of the POIs in the inertial and vision data, the pairing of the hand POIs to objects POIs that correspond to the same action in the task, and finally, the correction of the hand trajectory based on the pairs of POIs. The complete method for demonstration acquisition is experimentally evaluated in a full PbD process. This experiment reveals the advantages of the proposed method over kinesthesy in the context of this work.La programmation par démonstration est une approche intuitive permettant de transmettre une tâche à un robot à partir d'une ou plusieurs démonstrations faites par un enseignant humain. L'acquisition des démonstrations nécessite cependant la résolution d'un problème de correspondance quand les systèmes sensitifs et moteurs de l'enseignant et de l'apprenant diffèrent. De nombreux travaux utilisent des démonstrations faites par kinesthésie, i.e., l'enseignant manipule directement le robot pour lui faire faire la tâche. Ce dernier enregistre ses mouvements grâce à ses propres encodeurs. De cette façon, le problème de correspondance est trivial. Lors de telles démonstrations, la dextérité de l'enseignant peut être altérée et impacter tout le processus de programmation par démonstration. Les méthodes d'acquisition de démonstration moins invalidantes pour l'enseignant nécessitent souvent des procédures spécifiques pour résoudre le problème de correspondance. Ainsi l'acquisition des démonstrations se base sur un compromis entre complexité de ces procédures, le niveau de précision des éléments enregistrés et la commodité pour l'enseignant. Cette thèse propose ainsi une méthode de mesure du mouvement humain par capteurs inertiels pour la programmation par démonstration de tâches de ``pick-and-place''. Les capteurs inertiels sont en effet pratiques et faciles à utiliser, mais sont d'une précision limitée. Nous étudions leur potentiel pour la programmation par démonstration.
Pour estimer la trajectoire de la main de l'enseignant, des capteurs inertiels sont placés sur son bras, son avant-bras et sa main afin d'estimer leurs orientations. Une méthode est proposée afin de compenser partiellement la dérive de l'estimation de l'orientation des capteurs autour de la direction de la gravité. Cette méthode, appelée ``heading reset'', est basée sur l'hypothèse que le capteur passe plusieurs fois par son azimut initial avec des phases stationnaires lors d'une démonstration. Cette méthode est implémentée dans un algorithme d'intégration et d'observation de vecteur. Des expériences illustrent les avantages du ``heading reset''.
Cette thèse développe ensuite une méthode complète de mesure des mouvements de la main humaine par capteurs inertiels (IHMT). Elle comprend une première procédure d'initialisation pour estimer l'orientation des capteurs par rapport aux segments du bras humain ainsi que l'orientation initiale des capteurs par rapport au repère de référence de l'humain. Cette procédure, consistant en une rotation et une position statique du bras tendu, est robuste au positionnement des capteurs. Une seconde procédure est proposée pour estimer la position de l'humain par rapport au robot et pour calibrer les paramètres de la méthode. Finalement, l'erreur moyenne sur la trajectoire de la main humaine est mesurée expérimentalement entre 28.5 mm et 61.8 mm, ce qui n'est cependant pas suffisant pour la programmation par démonstration.
Afin d'atteindre le niveau de précision nécessaire, une nouvelle méthode est développée afin de corriger la trajectoire de la main par IHMT à partir de données issues d'un système de vision, complémentaire des capteurs inertiels. Pour maintenir une certaine simplicité et robustesse, le système de vision ne suit que les objets et pas l'enseignant. La méthode de correction, basée sur des ``Positions Of Interest (POIs)'', est constituée de 3 étapes: l'identification des POIs dans les données issues des capteurs inertiels et du système de vision, puis l'association de POIs liées à la main et de POIs liées aux objets correspondant à la même action, et enfin, la correction de la trajectoire de la main à partir des paires de POIs. Finalement, la méthode IHMT corrigée est expérimentalement évaluée dans un processus complet de programmation par démonstration. Cette expérience montre l'avantage de la méthode proposée sur la kinesthésie dans le contexte de ce travail
A Taxonomy of Freehand Grasping Patterns in Virtual Reality
Grasping is the most natural and primary interaction paradigm people perform every day, which allows us to pick up and manipulate objects around us such as drinking a cup of coffee or writing with a pen. Grasping has been highly explored in real environments, to understand and structure the way people grasp and interact with objects by presenting categories, models and theories for grasping approach. Due to the complexity of the human hand, classifying grasping knowledge to provide meaningful insights is a challenging task, which led to researchers developing grasp taxonomies to provide guidelines for emerging grasping work (such as in anthropology, robotics and hand surgery) in a systematic way.
While this body of work exists for real grasping, the nuances of grasping transfer in virtual environments is unexplored. The emerging development of robust hand tracking sensors for virtual devices now allow the development of grasp models that enable VR to simulate real grasping interactions. However, present work has not yet explored the differences and nuances that are present in virtual grasping compared to real object grasping, which means that virtual systems that create grasping models based on real grasping knowledge, might make assumptions which are yet to be proven true or untrue around the way users intuitively grasp and interact with virtual objects.
To address this, this thesis presents the first user elicitation studies to explore grasping patterns directly in VR. The first study presents main similarities and differences between real and virtual object grasping, the second study furthers this by exploring how virtual object shape influences grasping patterns, the third study focuses on visual thermal cues and how this influences grasp metrics, and the fourth study focuses on understanding other object characteristics such as stability and complexity and how they influence grasps in VR. To provide structured insights on grasping interactions in VR, the results are synthesized in the first VR Taxonomy of Grasp Types, developed following current methods for developing grasping and HCI taxonomies and re-iterated to
present an updated and more complete taxonomy.
Results show that users appear to mimic real grasping behaviour in VR, however they also illustrate that users present issues around object size estimation and generally a lower variability in grasp types is used. The taxonomy shows that only five grasps account for the majority of grasp data in VR, which can be used for computer systems aiming to achieve natural and intuitive interactions at lower computational cost. Further, findings show that virtual object characteristics such as shape, stability and complexity as well as visual cues for temperature influence grasp metrics such as aperture, category, type, location and dimension. These changes in grasping patterns together with virtual object categorisation methods can be used to inform design decisions when developing intuitive interactions and virtual objects and environments and therefore taking a step forward in achieving natural grasping interaction in VR
Expanding the User Interactions and Design Process of Haptic Experiences in Virtual Reality
Virtual reality can be a highly immersive experience due to its realistic visual presentation. This immersive state is useful for applications including education, training, and entertainment. To enhance the state of immersion provided by virtual reality further, devices capable of simulating touch and force have been researched to allow not only a visual and audio experience but a haptic experience as well. Such research has investigated many approaches to generating haptics for virtual reality but often does not explore how to create an immersive haptic experience using them. In this thesis, we present a discussion on four proposed areas of the virtual reality haptic experience design process using a demonstration methodology. To investigate the application of haptic devices, we designed a modular ungrounded haptic system which was used to create a general-purpose device capable of force-based feedback and used it in the three topics of exploration. The first area explored is the application of existing haptic theory for aircraft control to the field of virtual reality drone control. The second area explored is the presence of the size-weight sensory illusion within virtual reality when using a simulated haptic force. The third area explored is how authoring within a virtual reality medium can be used by a designer to create VR haptic experiences. From these explorations, we begin a higher-level discussion of the broader process of creating a virtual reality haptic experience. Using the results of each project as a representation of our proposed design steps, we discuss not only the broader concepts the steps contribute to the process and their importance, but also draw connections between them. By doing this, we present a more holistic approach to the large-scale design of virtual reality haptic experiences and the benefits we believe it provides
Teaching Unknown Objects by Leveraging Human Gaze and Augmented Reality in Human-Robot Interaction
Roboter finden aufgrund ihrer außergewöhnlichen Arbeitsleistung, Präzision, Effizienz und Skalierbarkeit immer mehr Verwendung in den verschiedensten Anwendungsbereichen. Diese Entwicklung wurde zusätzlich begünstigt durch Fortschritte in der Künstlichen Intelligenz (KI), insbesondere im Maschinellem Lernen (ML). Mit Hilfe moderner neuronaler Netze sind Roboter in der Lage, Objekte in ihrer Umgebung zu erkennen und mit ihnen zu interagieren. Ein erhebliches Manko besteht jedoch darin, dass das Training dieser Objekterkennungsmodelle, in aller Regel mit einer zugrundeliegenden Abhängig von umfangreichen Datensätzen und der Verfügbarkeit großer Datenmengen einhergeht. Dies ist insbesondere dann problematisch, wenn der konkrete Einsatzort des Roboters und die Umgebung, einschließlich der darin befindlichen Objekte, nicht im Voraus bekannt sind. Die breite und ständig wachsende Palette von Objekten macht es dabei praktisch unmöglich, das gesamte Spektrum an existierenden Objekten allein mit bereits zuvor erstellten Datensätzen vollständig abzudecken. Das Ziel dieser Dissertation war es, einem Roboter unbekannte Objekte mit Hilfe von Human-Robot Interaction (HRI) beizubringen, um ihn von seiner Abhängigkeit von Daten sowie den Einschränkungen durch vordefinierte Szenarien zu befreien. Die Synergie von Eye Tracking und Augmented Reality (AR) ermöglichte es dem als Lehrer fungierenden Menschen, mit dem Roboter zu kommunizieren und ihn mittels des menschlichen Blickes auf Objekte hinzuweisen. Dieser holistische Ansatz ermöglichte die Konzeption eines multimodalen HRI-Systems, durch das der Roboter Objekte identifizieren und dreidimensional segmentieren konnte, obwohl sie ihm zu diesem Zeitpunkt noch unbekannt waren, um sie anschließend aus unterschiedlichen Blickwinkeln eigenständig zu inspizieren. Anhand der Klasseninformationen, die ihm der Mensch mitteilte, war der Roboter daraufhin in der Lage, die entsprechenden Objekte zu erlernen und später wiederzuerkennen. Mit dem Wissen, das dem Roboter durch diesen auf HRI basierenden Lehrvorgang beigebracht worden war, war dessen Fähigkeit Objekte zu erkennen vergleichbar mit den Fähigkeiten modernster Objektdetektoren, die auf umfangreichen Datensätzen trainiert worden waren. Dabei war der Roboter jedoch nicht auf vordefinierte Klassen beschränkt, was seine Vielseitigkeit und Anpassungsfähigkeit unter Beweis stellte. Die im Rahmen dieser Dissertation durchgeführte Forschung leistete bedeutende Beiträge an der Schnittstelle von Machine Learning (ML), AR, Eye Tracking und Robotik. Diese Erkenntnisse tragen nicht nur zum besseren Verständnis der genannten Felder bei, sondern ebnen auch den Weg für weitere interdisziplinäre Forschung. Die in dieser Dissertation enthalten wissenschaftlichen Artikel wurden auf hochrangigen Konferenzen in den Bereichen Robotik, Eye Tracking und HRI veröffentlicht.Robots are becoming increasingly popular in a wide range of environments due to their exceptional work capacity, precision, efficiency, and scalability. This development has been further encouraged by advances in Artificial Intelligence (AI), particularly Machine Learning (ML). By employing sophisticated neural networks, robots are given the ability to detect and interact with objects in their vicinity. However, a significant drawback arises from the underlying dependency on extensive datasets and the availability of substantial amounts of training data for these object detection models. This issue becomes particularly problematic when the specific deployment location of the robot and the surroundings, including the objects within it, are not known in advance. The vast and ever-expanding array of objects makes it virtually impossible to comprehensively cover the entire spectrum of existing objects using preexisting datasets alone. The goal of this dissertation was to teach a robot unknown objects in the context of Human-Robot Interaction (HRI) in order to liberate it from its data dependency, unleashing it from predefined scenarios. In this context, the combination of eye tracking and Augmented Reality (AR) created a powerful synergy that empowered the human teacher to seamlessly communicate with the robot and effortlessly point out objects by means of human gaze. This holistic approach led to the development of a multimodal HRI system that enabled the robot to identify and visually segment the Objects of Interest (OOIs) in three-dimensional space, even though they were initially unknown to it, and then examine them autonomously from different angles. Through the class information provided by the human, the robot was able to learn the objects and redetect them at a later stage. Due to the knowledge gained from this HRI based teaching process, the robot’s object detection capabilities exhibited comparable performance to state-of-the-art object detectors trained on extensive datasets, without being restricted to predefined classes, showcasing its versatility and adaptability. The research conducted within the scope of this dissertation made significant contributions at the intersection of ML, AR, eye tracking, and robotics. These findings not only enhance the understanding of these fields, but also pave the way for further interdisciplinary research. The scientific articles included in this dissertation have been published at high-impact conferences in the fields of robotics, eye tracking, and HRI
Digital Twins for Industry 4.0 in the 6G Era
Having the Fifth Generation (5G) mobile communication system recently rolled
out in many countries, the wireless community is now setting its eyes on the
next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial
use cases, 6G is envisaged to become the infrastructural backbone of future
intelligent industry. Especially, a combination of 6G and the emerging
technologies of Digital Twins (DT) will give impetus to the next evolution of
Industry 4.0 (I4.0) systems. This article provides a survey in the research
area of 6G-empowered industrial DT system. With a novel vision of 6G industrial
DT ecosystem, this survey discusses the ambitions and potential applications of
industrial DT in the 6G era, identifying the emerging challenges as well as the
key enabling technologies. The introduced ecosystem is supposed to bridge the
gaps between humans, machines, and the data infrastructure, and therewith
enable numerous novel application scenarios.Comment: Accepted for publication in IEEE Open Journal of Vehicular Technolog
From Industry to Practice: Can Users Tackle Domain Tasks with Augmented Reality?
Augmented Reality (AR) is a cutting-edge interactive technology. While Virtual Reality (VR) is based on completely virtual and immersive environments, AR superimposes virtual objects onto the real world. The value of AR has been demonstrated and applied within numerous industrial application areas due to its capability of providing interactive interfaces of visualized digital content. AR can provide functional tools that support users in undertaking domain-related tasks, especially facilitating them in data visualization and interaction by jointly augmenting physical space and user perception. Making effective use of the advantages of AR, especially the ability which augment human vision to help users perform different domain-related tasks is the central part of my PhD research.Industrial process tomography (IPT), as a non-intrusive and commonly-used imaging technique, has been effectively harnessed in many manufacturing components for inspections, monitoring, product quality control, and safety issues. IPT underpins and facilitates the extraction of qualitative and quantitative data regarding the related industrial processes, which is usually visualized in various ways for users to understand its nature, measure the critical process characteristics, and implement process control in a complete feedback network. The adoption of AR in benefiting IPT and its related fields is currently still scarce, resulting in a gap between AR technique and industrial applications. This thesis establishes a bridge between AR practitioners and IPT users by accomplishing four stages. First of these is a need-finding study of how IPT users can harness AR technique was developed. Second, a conceptualized AR framework, together with the implemented mobile AR application developed in an optical see-through (OST) head-mounted display (HMD) was proposed. Third, the complete approach for IPT users interacting with tomographic visualizations as well as the user study was investigated.Based on the shared technologies from industry, we propose and examine an AR approach for visual search tasks providing visual hints, audio hints, and gaze-assisted instant post-task feedback as the fourth stage. The target case was a book-searching task, in which we aimed to explore the effect of the hints and the feedback with two hypotheses: that both visual and audio hints can positively affect AR search tasks whilst the combination outperforms the individuals; that instant post-task feedback can positively affect AR search tasks. The proof-of-concept was demonstrated by an AR app in an HMD with a two-stage user evaluation. The first one was a pilot study (n=8) where the impact of the visual hint in benefiting search task performance was identified. The second was a comprehensive user study (n=96) consisting of two sub-studies, Study I (n=48) and Study II (n=48). Following quantitative and qualitative analysis, our results partially verified the first hypothesis and completely verified the second, enabling us to conclude that the synthesis of visual and audio hints conditionally improves AR search task efficiency when coupled with task feedback
AdaptiX -- A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics
With the ongoing efforts to empower people with mobility impairments and the
increase in technological acceptance by the general public, assistive
technologies, such as collaborative robotic arms, are gaining popularity. Yet,
their widespread success is limited by usability issues, specifically the
disparity between user input and software control along the autonomy continuum.
To address this, shared control concepts provide opportunities to combine the
targeted increase of user autonomy with a certain level of computer assistance.
This paper presents the free and open-source AdaptiX XR framework for
developing and evaluating shared control applications in a high-resolution
simulation environment. The initial framework consists of a simulated robotic
arm with an example scenario in Virtual Reality (VR), multiple standard control
interfaces, and a specialized recording/replay system. AdaptiX can easily be
extended for specific research needs, allowing Human-Robot Interaction (HRI)
researchers to rapidly design and test novel interaction methods, intervention
strategies, and multi-modal feedback techniques, without requiring an actual
physical robotic arm during the early phases of ideation, prototyping, and
evaluation. Also, a Robot Operating System (ROS) integration enables the
controlling of a real robotic arm in a PhysicalTwin approach without any
simulation-reality gap. Here, we review the capabilities and limitations of
AdaptiX in detail and present three bodies of research based on the framework.
AdaptiX can be accessed at https://adaptix.robot-research.de.Comment: Accepted submission at The 16th ACM SIGCHI Symposium on Engineering
Interactive Computing Systems (EICS'24
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