118 research outputs found

    Constraint-based simulation of virtual crowds

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    Central to simulating pedestrian crowds is their motion and behaviour. It is required to understand how pedestrians move to simulate and predict scenarios with crowds of people. Pedestrian behaviours enhance the range of motions people can demonstrate, resulting in greater variety, believability, and accuracy. Models with complex computations and motion have difficulty in being extended with additional behaviours. This is because the structure of these models are not designed in a way that is generally compatible with collision avoidance behaviours. To address this issue, this thesis will research a possible pedestrian model that can simulate collision response with a wide range of additional behaviours. The model will do so by using constraints, a limit on the velocity of a person's movement. The proposed model will use constraints as the core computation. By describing behaviours in terms of constraints, these behaviours can be combined with the proposed model. Pedestrian simulations strike a balance between model complexity and runtime speed. Some models focus entirely on the complexity and accuracy of people, while other models focus on creating believable yet lightweight and performant simulations. Believable crowds look realistic to human observation, but do not match up to numerical analysis under scrutiny. The larger the population, and the more complex the motion of people, the slower the simulation will run. One route for improving performance of software is by using Graphical Processing Units (GPUs). GPUs are devices with theoretical performance that far outperforms equivalent multi-core CPUs. Research literature tends to focus on either the accuracy, or the performance optimisations of pedestrian crowd simulations. This suggests that there is opportunity to create more accurate models that run relatively quickly. Real time is a useful measure of model runtime. A simulation that runs in real time can be interactive and respond live to user input. By increasing the performance of the model, larger and more complex models can be simulated. This in turn increases the range of applications the model can represent. This thesis will develop a performant pedestrian simulation that runs in real time. It will explore how suitable the model is for GPU acceleration, and what performance gains can be obtained by implementing the model on the GPU

    Social aspects of collision avoidance: A detailed analysis of two-person groups and individual pedestrians

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    Pedestrian groups are commonly found in crowds but research on their social aspects is comparatively lacking. To fill that void in literature, we study the dynamics of collision avoidance between pedestrian groups (in particular dyads) and individual pedestrians in an ecological environment, focusing in particular on (i) how such avoidance depends on the group's social relation (e.g. colleagues, couples, friends or families) and (ii) its intensity of social interaction (indicated by conversation, gaze exchange, gestures etc). By analyzing relative collision avoidance in the ``center of mass'' frame, we were able to quantify how much groups and individuals avoid each other with respect to the aforementioned properties of the group. A mathematical representation using a potential energy function is proposed to model avoidance and it is shown to provide a fair approximation to the empirical observations. We also studied the probability that the individuals disrupt the group by ``passing through it'' (termed as intrusion). We analyzed the dependence of the parameters of the avoidance model and of the probability of intrusion on groups' social relation and intensity of interaction. We confirmed that the stronger social bonding or interaction intensity is, the more prominent collision avoidance turns out. We also confirmed that the probability of intrusion is a decreasing function of interaction intensity and strength of social bonding. Our results suggest that such variability should be accounted for in models and crowd management in general. Namely, public spaces with strongly bonded groups (e.g. a family-oriented amusement park) may require a different approach compared to public spaces with loosely bonded groups (e.g. a business-oriented trade fair).Comment: 25 pages, 15 figures, 3 table

    Shared Perception in Human-Robot Interaction

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    Interaction can be seen as a composition of perspectives: the integration of perceptions, intentions, and actions on the environment two or more agents share. For an interaction to be effective, each agent must be prone to “sharedness”: being situated in a common environment, able to read what others express about their perspective, and ready to adjust one’s own perspective accordingly. In this sense, effective interaction is supported by perceiving the environment jointly with others, a capability that in this research is called Shared Perception. Nonetheless, perception is a complex process that brings the observer receiving sensory inputs from the external world and interpreting them based on its own, previous experiences, predictions, and intentions. In addition, social interaction itself contributes to shaping what is perceived: others’ attention, perspective, actions, and internal states may also be incorporated into perception. Thus, Shared perception reflects the observer's ability to integrate these three sources of information: the environment, the self, and other agents. If Shared Perception is essential among humans, it is equally crucial for interaction with robots, which need social and cognitive abilities to interact with humans naturally and successfully. This research deals with Shared Perception within the context of Social Human-Robot Interaction (HRI) and involves an interdisciplinary approach. The two general axes of the thesis are the investigation of human perception while interacting with robots and the modeling of robot’s perception while interacting with humans. Such two directions are outlined through three specific Research Objectives, whose achievements represent the contribution of this work. i) The formulation of a theoretical framework of Shared Perception in HRI valid for interpreting and developing different socio-perceptual mechanisms and abilities. ii) The investigation of Shared Perception in humans focusing on the perceptual mechanism of Context Dependency, and therefore exploring how social interaction affects the use of previous experience in human spatial perception. iii) The implementation of a deep-learning model for Addressee Estimation to foster robots’ socio-perceptual skills through the awareness of others’ behavior, as suggested in the Shared Perception framework. To achieve the first Research Objective, several human socio-perceptual mechanisms are presented and interpreted in a unified account. This exposition parallels mechanisms elicited by interaction with humans and humanoid robots and aims to build a framework valid to investigate human perception in the context of HRI. Based on the thought of D. Davidson and conceived as the integration of information coming from the environment, the self, and other agents, the idea of "triangulation" expresses the critical dynamics of Shared Perception. Also, it is proposed as the functional structure to support the implementation of socio-perceptual skills in robots. This general framework serves as a reference to fulfill the other two Research Objectives, which explore specific aspects of Shared Perception. For what concerns the second Research Objective, the human perceptual mechanism of Context Dependency is investigated, for the first time, within social interaction. Human perception is based on unconscious inference, where sensory inputs integrate with prior information. This phenomenon helps in facing the uncertainty of the external world with predictions built upon previous experience. To investigate the effect of social interaction on such a mechanism, the iCub robot has been used as an experimental tool to create an interactive scenario with a controlled setting. A user study based on psychophysical methods, Bayesian modeling, and a neural network analysis of human results demonstrated that social interaction influenced Context Dependency so that when interacting with a social agent, humans rely less on their internal models and more on external stimuli. Such results are framed in Shared Perception and contribute to revealing the integration dynamics of the three sources of Shared Perception. The others’ presence and social behavior (other agents) affect the balance between sensory inputs (environment) and personal history (self) in favor of the information shared with others, that is, the environment. The third Research Objective consists of tackling the Addressee Estimation problem, i.e., understanding to whom a speaker is talking, to improve the iCub social behavior in multi-party interactions. Addressee Estimation can be considered a Shared Perception ability because it is achieved by using sensory information from the environment, internal representations of the agents’ position, and, more importantly, the understanding of others’ behavior. An architecture for Addressee Estimation is thus designed considering the integration process of Shared Perception (environment, self, other agents) and partially implemented with respect to the third element: the awareness of others’ behavior. To achieve this, a hybrid deep-learning (CNN+LSTM) model is developed to estimate the speaker-robot relative placement of the addressee based on the non-verbal behavior of the speaker. Addressee Estimation abilities based on Shared Perception dynamics are aimed at improving multi-party HRI. Making robots aware of other agents’ behavior towards the environment is the first crucial step for incorporating such information into the robot’s perception and modeling Shared Perception

    Dimensionless Numbers Reveal Distinct Regimes in the Structure and Dynamics of Pedestrian Crowds

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    In fluid mechanics, dimensionless numbers like the Reynolds number help classify flows. We argue that such a classification is also relevant for crowd flows by putting forward the dimensionless Intrusion and Avoidance numbers.Using an extensive dataset, we show that these delineate regimes that are characterized by distinct structural signatures, best probed in terms of distances at low Avoidance number and times-to-collision at low Intrusion number.These findings prompt a perturbative expansion of the agent-based dynamics; the generic models thus obtained perform well in (and only in) the regime in which they were derived

    Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

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    A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this paper, we pave the road towards common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.Comment: 43 pages, 11 figures, 6 table

    Thinking for the bound and dead: beyond MAN3 towards a new (truly) universal theory of human victory

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    This project is a blend of Africana intellectual history and philosophical anti-humanism. The opening chapter seeks to contextualize the thought of Huey P. Newton in the Black nationalist tradition outline his conceptualization of US empire – ‘Reactionary Intercommunalism’. I use the second chapter to explore counterinsurgency as a historical phenomenon that laid the basis for European colonization and the civilizing mission during the 17th, 18th and 19th centuries and the modern phenomena understand as racial violence. The third chapter analyzes how gender ideological have been deployed toward this end historically and through contemporaneous Black scholarship before using the final chapter to introduce the theory of killology or MAN3. This theory advances the claim that counterinsurgency as a modality of warfare be understood as the contemporary fountainhead of western humanism and thus as the primary force of social regulation which proleptically organizes modern civilization on a spatially and temporally indeterminate basis to defeat/subvert insurgent populations before they are ever mobilized towards resistance through the application of technology, deadly force to those constructed as threats and control of the information environment towards this end

    Transforming our World through Universal Design for Human Development

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    An environment, or any building product or service in it, should ideally be designed to meet the needs of all those who wish to use it. Universal Design is the design and composition of environments, products, and services so that they can be accessed, understood and used to the greatest extent possible by all people, regardless of their age, size, ability or disability. It creates products, services and environments that meet people’s needs. In short, Universal Design is good design. This book presents the proceedings of UD2022, the 6th International Conference on Universal Design, held from 7 - 9 September 2022 in Brescia, Italy.The conference is targeted at professionals and academics interested in the theme of universal design as related to the built environment and the wellbeing of users, but also covers mobility and urban environments, knowledge, and information transfer, bringing together research knowledge and best practice from all over the world. The book contains 72 papers from 13 countries, grouped into 8 sections and covering topics including the design of inclusive natural environments and urban spaces, communities, neighborhoods and cities; housing; healthcare; mobility and transport systems; and universally- designed learning environments, work places, cultural and recreational spaces. One section is devoted to universal design and cultural heritage, which had a particular focus at this edition of the conference. The book reflects the professional and disciplinary diversity represented in the UD movement, and will be of interest to all those whose work involves inclusive design

    Sonic Interactions in Virtual Environments

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