Designing Disambiguation Techniques for Pointing in the Physical World

International audienceSeveral ways for selecting physical objects exist, including touching and pointing at them. Allowing the user to interact at a distance by pointing at physical objects can be challenging when the environment contains a large number of interactive physical objects, possibly occluded by other everyday items. Previous pointing techniques highlighted the need for disambiguation techniques. Addressing this challenge, this paper contributes a design space that organizes along groups and axes a set of options for designers to relevantly (1) describe, (2) classify, and (3) design disambiguation techniques. First, we have not found techniques in the literature yet that our design space could not describe. Second, all the techniques show a different path along the axes of our design space. Third, it allows defining of several new paths/solutions that have not yet been explored. We illustrate this generative power with the example of such a designed technique, Physical Pointing Roll (P2Roll)

Mobile Pointing Task in the Physical World: Balancing Focus and Performance while Disambiguating

International audienceWe address the problem of mobile distal selection of physical objects when pointing at them in augmented environments. We focus on the disambiguation step needed when several objects are selected with a rough pointing gesture. A usual disambiguation technique forces the users to switch their focus from the physical world to a list displayed on a handheld device's screen. In this paper, we explore the balance between change of users' focus and performance. We present two novel interaction techniques allowing the users to maintain their focus in the physical world. Both use a cycling mechanism, respectively performed with a wrist rolling gesture for P2Roll or with a finger sliding gesture for P2Slide. A user experiment showed that keeping users' focus in the physical world outperforms techniques that require the users to switch their focus to a digital representation distant from the physical objects, when disambiguating up to 8 objects

An Introduction to 3D User Interface Design

3D user interface design is a critical component of any virtual environment (VE) application. In this paper, we present a broad overview of three-dimensional (3D) interaction and user interfaces. We discuss the effect of common VE hardware devices on user interaction, as well as interaction techniques for generic 3D tasks and the use of traditional two-dimensional interaction styles in 3D environments. We divide most user interaction tasks into three categories: navigation, selection/manipulation, and system control. Throughout the paper, our focus is on presenting not only the available techniques, but also practical guidelines for 3D interaction design and widely held myths. Finally, we briefly discuss two approaches to 3D interaction design, and some example applications with complex 3D interaction requirements. We also present an annotated online bibliography as a reference companion to this article

A Voice and Pointing Gesture Interaction System for Supporting Human Spontaneous Decisions in Autonomous Cars

Autonomous cars are expected to improve road safety, traffic and mobility. It is projected that in the next 20-30 years fully autonomous vehicles will be on the market. The advancement on the research and development of this technology will allow the disengagement of humans from the driving task, which will be responsibility of the vehicle intelligence. In this scenario new vehicle interior designs are proposed, enabling more flexible human vehicle interactions inside them. In addition, as some important stakeholders propose, control elements such as the steering wheel and accelerator and brake pedals may not be needed any longer. However, this user control disengagement is one of the main issues related with the user acceptance of this technology. Users do not seem to be comfortable with the idea of giving all the decision power to the vehicle. In addition, there can be location awareness situations where the user makes a spontaneous decision and requires some type of vehicle control. Such is the case of stopping at a particular point of interest or taking a detour in the pre-calculated autonomous route of the car. Vehicle manufacturers\u27 maintain the steering wheel as a control element, allowing the driver to take over the vehicle if needed or wanted. This causes a constraint in the previously mentioned human vehicle interaction flexibility. Thus, there is an unsolved dilemma between providing users enough control over the autonomous vehicle and route so they can make spontaneous decision, and interaction flexibility inside the car. This dissertation proposes the use of a voice and pointing gesture human vehicle interaction system to solve this dilemma. Voice and pointing gestures have been identified as natural interaction techniques to guide and command mobile robots, potentially providing the needed user control over the car. On the other hand, they can be executed anywhere inside the vehicle, enabling interaction flexibility. The objective of this dissertation is to provide a strategy to support this system. For this, a method based on pointing rays intersections for the computation of the point of interest (POI) that the user is pointing to is developed. Simulation results show that this POI computation method outperforms the traditional ray-casting based by 76.5% in cluttered environments and 36.25% in combined cluttered and non-cluttered scenarios. The whole system is developed and demonstrated using a robotics simulator framework. The simulations show how voice and pointing commands performed by the user update the predefined autonomous path, based on the recognized command semantics. In addition, a dialog feedback strategy is proposed to solve conflicting situations such as ambiguity in the POI identification. This additional step is able to solve all the previously mentioned POI computation inaccuracies. In addition, it allows the user to confirm, correct or reject the performed commands in case the system misunderstands them

Intelligent Selection Techniques For Virtual Environments

Selection in 3D games and simulations is a well-studied problem. Many techniques have been created to address many of the typical scenarios a user could experience. For any single scenario with consistent conditions, there is likely a technique which is well suited. If there isn\u27t, then there is an opportunity for one to be created to best suit the expected conditions of that new scenario. It is critical that the user be given an appropriate technique to interact with their environment. Without it, the entire experience is at risk of becoming burdensome and not enjoyable. With all of the different possible scenarios, it can become problematic when two or more are part of the same program. If they are put closely together, or even intertwined, then the developer is often forced to pick a single technique that works so-so in both, but is likely not optimal for either, or maybe optimal in just one of them. In this case, the user is left to perform selections with a technique that is lacking in one way or another, which can increase errors and frustration. In our research, we have outlined different selection scenarios, all of which were classified by their level of object density (number of objects in scene) and object velocity. We then performed an initial study on how it impacts performance of various selection techniques, including a new selection technique that we developed just for this test, called Expand. Our results showed, among other things, that a standard Raycast technique works well in slow moving and sparse environments, while revealing that our new Expand technique works well in denser environments. With the results from our first study, we sought to develop something that would bridge the gap in performance between those selection techniques tested. Our idea was a framework that could harvest several different selection techniques and determine which was the most optimal at any time. Each selection technique would report how effective it was, given the provided scenario conditions. The framework was responsible for activating the appropriate selection technique when the user made a selection attempt. With this framework in hand, we performed two additional user studies to determine how effective it could be in actual use, and to identify its strengths and weaknesses. Each study compared several selection techniques individually against the framework which utilized them collectively, picking the most suitable. Again, the same scenarios from our first study were reused. From these studies, we gained a deeper understanding of the many challenges associated with automatic selection technique determination. The results from these two studies showed that transitioning between techniques was potentially viable, but rife with design challenges that made its optimization quite difficult. In an effort to sidestep some of the issues surrounding the switching of discrete techniques, we sought to attack the problem from the other direction, and make a single technique act similarly to two techniques, adjusting dynamically to conditions. We performed a user study to analyze the performance of such a technique, with promising results. While the qualitative differences were small, the user feedback did indicate that users preferred this technique over the others, which were static in nature. Finally, we sought to gain a deeper understanding of existing selection techniques that were dynamic in nature, and study how they were designed, and how they could be improved. We scrutinized the attributes of each technique that were already being adjusted dynamically or that could be adjusted and innovated new ways in which the technique could be improved upon. Within this analysis, we also gave thought to how each technique could be best integrated into the Auto-Select framework we proposed earlier. This overall analysis of the latest selection techniques left us with an array of new variants that warrant being created and tested against their existing versions. Our overall research goal was to perform an analysis of selection techniques that intelligently adapt to their environment. We believe that we achieved this by performing several iterative development cycles, including user studies and ultimately leading to innovation in the field of selection. We conclude our research with yet more questions left to be answered. We intend to pursue further research regarding some of these questions, as time permits

Gestures in human-robot interaction

Gesten sind ein Kommunikationsweg, der einem Betrachter Informationen oder Absichten übermittelt. Daher können sie effektiv in der Mensch-Roboter-Interaktion, oder in der Mensch-Maschine-Interaktion allgemein, verwendet werden. Sie stellen eine Möglichkeit für einen Roboter oder eine Maschine dar, um eine Bedeutung abzuleiten. Um Gesten intuitiv benutzen zukönnen und Gesten, die von Robotern ausgeführt werden, zu verstehen, ist es notwendig, Zuordnungen zwischen Gesten und den damit verbundenen Bedeutungen zu definieren -- ein Gestenvokabular. Ein Menschgestenvokabular definiert welche Gesten ein Personenkreis intuitiv verwendet, um Informationen zu übermitteln. Ein Robotergestenvokabular zeigt welche Robotergesten zu welcher Bedeutung passen. Ihre effektive und intuitive Benutzung hängt von Gestenerkennung ab, das heißt von der Klassifizierung der Körperbewegung in diskrete Gestenklassen durch die Verwendung von Mustererkennung und maschinellem Lernen. Die vorliegende Dissertation befasst sich mit beiden Forschungsbereichen. Als eine Voraussetzung für die intuitive Mensch-Roboter-Interaktion wird zunächst ein Aufmerksamkeitsmodell für humanoide Roboter entwickelt. Danach wird ein Verfahren für die Festlegung von Gestenvokabulare vorgelegt, das auf Beobachtungen von Benutzern und Umfragen beruht. Anschliessend werden experimentelle Ergebnisse vorgestellt. Eine Methode zur Verfeinerung der Robotergesten wird entwickelt, die auf interaktiven genetischen Algorithmen basiert. Ein robuster und performanter Gestenerkennungsalgorithmus wird entwickelt, der auf Dynamic Time Warping basiert, und sich durch die Verwendung von One-Shot-Learning auszeichnet, das heißt durch die Verwendung einer geringen Anzahl von Trainingsgesten. Der Algorithmus kann in realen Szenarien verwendet werden, womit er den Einfluss von Umweltbedingungen und Gesteneigenschaften, senkt. Schließlich wird eine Methode für das Lernen der Beziehungen zwischen Selbstbewegung und Zeigegesten vorgestellt.Gestures consist of movements of body parts and are a mean of communication that conveys information or intentions to an observer. Therefore, they can be effectively used in human-robot interaction, or in general in human-machine interaction, as a way for a robot or a machine to infer a meaning. In order for people to intuitively use gestures and understand robot gestures, it is necessary to define mappings between gestures and their associated meanings -- a gesture vocabulary. Human gesture vocabulary defines which gestures a group of people would intuitively use to convey information, while robot gesture vocabulary displays which robot gestures are deemed as fitting for a particular meaning. Effective use of vocabularies depends on techniques for gesture recognition, which considers classification of body motion into discrete gesture classes, relying on pattern recognition and machine learning. This thesis addresses both research areas, presenting development of gesture vocabularies as well as gesture recognition techniques, focusing on hand and arm gestures. Attentional models for humanoid robots were developed as a prerequisite for human-robot interaction and a precursor to gesture recognition. A method for defining gesture vocabularies for humans and robots, based on user observations and surveys, is explained and experimental results are presented. As a result of the robot gesture vocabulary experiment, an evolutionary-based approach for refinement of robot gestures is introduced, based on interactive genetic algorithms. A robust and well-performing gesture recognition algorithm based on dynamic time warping has been developed. Most importantly, it employs one-shot learning, meaning that it can be trained using a low number of training samples and employed in real-life scenarios, lowering the effect of environmental constraints and gesture features. Finally, an approach for learning a relation between self-motion and pointing gestures is presented

Sélection et Contrôle à Distance d'Objets Physiques Augmentés

International audienceNotre recherche doctorale concerne l'interaction dans les environnements intelligents. Plus particulièrement, nous considérons la sélection et le contrôle à distance d'objets physiques augmentés. Nos objectifs sont à la fois conceptuels, par la mise en place d'un espace de conception mais aussi pratiques par la conception, le développement et l'évaluation de techniques d'interaction. Nos résultats ont permis de souligner où l'attention de l'utilisateur doit être pour la sélection efficace et plaisante des objets augmentés à travers la comparaison expérimentale de deux nouvelles techniques de sélection d'objets physiques : P2Roll et P2Slide. Les perspectives en vue de la complétude des travaux concernent principalement le contrôle d'objets et incluent (1) l'évaluation des techniques de guidage pour le contrôle gestuel des objets augmentés par un utilisateur novice, et (2) l'évaluation in situ des techniques conçues

Crossmodal content binding in information-processing architectures

Operating in a physical context, an intelligent robot faces two fundamental problems. First, it needs to combine information from its different sensors to form a representation of the environment that is more complete than any of its sensors on its own could provide. Second, it needs to combine high-level representations (such as those for planning and dialogue) with its sensory information, to ensure that the interpretations of these symbolic representations are grounded in the situated context. Previous approaches to this problem have used techniques such as (low-level) information fusion, ontological reasoning, and (high-level) concept learning. This paper presents a framework in which these, and other approaches, can be combined to form a shared representation of the current state of the robot in relation to its environment and other agents. Preliminary results from an implemented system are presented to illustrate how the framework supports behaviours commonly required of an intelligent robot

3D Pointing with Everyday Devices: Speed, Occlusion, Fatigue

In recent years, display technology has evolved to the point where displays can be both non-stereoscopic and stereoscopic, and 3D environments can be rendered realistically on many types of displays. From movie theatres and shopping malls to conference rooms and research labs, 3D information can be deployed seamlessly. Yet, while 3D environments are commonly displayed in desktop settings, there are virtually no examples of interactive 3D environments deployed within ubiquitous environments, with the exception of console gaming. At the same time, immersive 3D environments remain - in users' minds - associated with professional work settings and virtual reality laboratories. An excellent opportunity for 3D interactive engagements is being missed not because of economic factors, but due to the lack of interaction techniques that are easy to use in ubiquitous, everyday environments. In my dissertation, I address the lack of support for interaction with 3D environments in ubiquitous settings by designing, implementing, and evaluating 3D pointing techniques that leverage a smartphone or a smartwatch as an input device. I show that mobile and wearable devices may be especially beneficial as input devices for casual use scenarios, where specialized 3D interaction hardware may be impractical, too expensive or unavailable. Such scenarios include interactions with home theatres, intelligent homes, in workplaces and classrooms, with movie theatre screens, in shopping malls, at airports, during conference presentations and countless other places and situations. Another contribution of my research is to increase the potential of mobile and wearable devices for efficient interaction at a distance. I do so by showing that such interactions are feasible when realized with the support of a modern smartphone or smartwatch. I also show how multimodality, when realized with everyday devices, expands and supports 3D pointing. In particular, I show how multimodality helps to address the challenges of 3D interaction: performance issues related to the limitations of the human motor system, interaction with occluded objects and related problem of perception of depth on non-stereoscopic screens, and user subjective fatigue, measured with NASA TLX as perceived workload, that results from providing spatial input for a prolonged time. I deliver these contributions by designing three novel 3D pointing techniques that support casual, "walk-up-and-use" interaction at a distance and are fully realizable using off-the-shelf mobile and wearable devices available today. The contributions provide evidence that democratization of 3D interaction can be realized by leveraging the pervasiveness of a device that users already carry with them: a smartphone or a smartwatch.4 month