588 research outputs found

    A flexible component-based robot control architecture for hormonal modulation of behaviour and affect

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    This document is the Accepted Manuscritpt of a paper published in Proceedings of 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017. Under embargo. Embargo end date: 20 July 2018. The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-319-64107-2_36. © 2017 Springer, Cham.In this paper we present the foundations of an architecture that will support the wider context of our work, which is to explore the link between affect, perception and behaviour from an embodied perspective and assess their relevance to Human Robot Interaction (HRI). Our approach builds upon existing affect-based architectures by combining artificial hormones with discrete abstract components that are designed with the explicit consideration of influencing, and being receptive to, the wider affective state of the robot

    The morphofunctional approach to emotion modelling in robotics

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    In this conceptual paper, we discuss two areas of research in robotics, robotic models of emotion and morphofunctional machines, and we explore the scope for potential cross-fertilization between them. We shift the focus in robot models of emotion from information-theoretic aspects of appraisal to the interactive significance of bodily dispositions. Typical emotional phenomena such as arousal and action readiness can be interpreted as morphofunctional processes, and their functionality may be replicated in robotic systems with morphologies that can be modulated for real-time adaptation. We investigate the control requirements for such systems, and present a possible bio-inspired architecture, based on the division of control between neural and endocrine systems in humans and animals. We suggest that emotional epi- sodes can be understood as emergent from the coordination of action control and action-readiness, respectively. This stress on morphology complements existing research on the information-theoretic aspects of emotion

    The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

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    © 2022 Khan and Cañamero. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/In dynamic (social) environments, an affective state of “stress” can be adaptive and promote agent wellbeing, but maladaptive if not appropriately regulated. The presence of (and interactions with) affect-based social support has been hypothesised to provide mechanisms to regulate stress (the “social buffering” hypothesis), though the precise, underlying mechanisms are still unclear. However, the hormone oxytocin has been implicated in mediating these effects in at least two ways: by improving social appraisals and reducing the short-term release of stress hormones (i.e., cortisol), and adapting an agent’s long-term stress tolerance. These effects likely facilitate an agent’s long-term adaptive ability by grounding their physiological and behavioural adaptation in the (affective) social environment, though these effects also appear to be context-dependent. In this paper, we investigate whether two of the hypothesised hormonal mechanisms that underpin the “social buffering” phenomenon affect the long-term wellbeing of (artificial) social agents who share affective social bonds, across numerous social and physical environmental contexts. Building on previous findings, we hypothesise that “social buffering” effects can improve the long-term wellbeing of agents who share affective social bonds in dynamic environments, through regular prosocial interactions with social bond partners. We model some of the effects associated with oxytocin and cortisol that underpin these hypothesised mechanisms in our biologically-inspired, socially-adaptive agent model, and conduct our investigation in a small society of artificial agents whose goal is to survive in challenging environments. Our results find that, while stress can be adaptive and regulated through affective social support, long-term behavioural and physiological adaptation is determined by the contextual perception of affective social bonds, which is influenced by early-stage interactions between affective social bond partners as well as the degree of the physical and social challenges. We also show how these low-level effects associated with oxytocin and cortisol can be used as “biomarkers” of social support and environmental stress. For socially-situated artificial agents, we suggest that these “social buffering” mechanisms can adapt the (adaptive) stress mechanisms, but that the long-term efficacy of this adaptation is related to the temporal dynamics of social interactions and the contextual perception of the affective social and physical environments.Peer reviewe

    Autonomous Decision-Making based on Biological Adaptive Processes for Intelligent Social Robots

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    MenciĂłn Internacional en el tĂ­tulo de doctorThe unceasing development of autonomous robots in many different scenarios drives a new revolution to improve our quality of life. Recent advances in human-robot interaction and machine learning extend robots to social scenarios, where these systems pretend to assist humans in diverse tasks. Thus, social robots are nowadays becoming real in many applications like education, healthcare, entertainment, or assistance. Complex environments demand that social robots present adaptive mechanisms to overcome different situations and successfully execute their tasks. Thus, considering the previous ideas, making autonomous and appropriate decisions is essential to exhibit reasonable behaviour and operate well in dynamic scenarios. Decision-making systems provide artificial agents with the capacity of making decisions about how to behave depending on input information from the environment. In the last decades, human decision-making has served researchers as an inspiration to endow robots with similar deliberation. Especially in social robotics, where people expect to interact with machines with human-like capabilities, biologically inspired decisionmaking systems have demonstrated great potential and interest. Thereby, it is expected that these systems will continue providing a solid biological background and improve the naturalness of the human-robot interaction, usability, and the acceptance of social robots in the following years. This thesis presents a decision-making system for social robots acting in healthcare, entertainment, and assistance with autonomous behaviour. The system’s goal is to provide robots with natural and fluid human-robot interaction during the realisation of their tasks. The decision-making system integrates into an already existing software architecture with different modules that manage human-robot interaction, perception, or expressiveness. Inside this architecture, the decision-making system decides which behaviour the robot has to execute after evaluating information received from different modules in the architecture. These modules provide structured data about planned activities, perceptions, and artificial biological processes that evolve with time that are the basis for natural behaviour. The natural behaviour of the robot comes from the evolution of biological variables that emulate biological processes occurring in humans. We also propose a Motivational model, a module that emulates biological processes in humans for generating an artificial physiological and psychological state that influences the robot’s decision-making. These processes emulate the natural biological rhythms of the human organism to produce biologically inspired decisions that improve the naturalness exhibited by the robot during human-robot interactions. The robot’s decisions also depend on what the robot perceives from the environment, planned events listed in the robot’s agenda, and the unique features of the user interacting with the robot. The robot’s decisions depend on many internal and external factors that influence how the robot behaves. Users are the most critical stimuli the robot perceives since they are the cornerstone of interaction. Social robots have to focus on assisting people in their daily tasks, considering that each person has different features and preferences. Thus, a robot devised for social interaction has to adapt its decisions to people that aim at interacting with it. The first step towards adapting to different users is identifying the user it interacts with. Then, it has to gather as much information as possible and personalise the interaction. The information about each user has to be actively updated if necessary since outdated information may lead the user to refuse the robot. Considering these facts, this work tackles the user adaptation in three different ways. ‱ The robot incorporates user profiling methods to continuously gather information from the user using direct and indirect feedback methods. ‱ The robot has a Preference Learning System that predicts and adjusts the user’s preferences to the robot’s activities during the interaction. ‱ An Action-based Learning System grounded on Reinforcement Learning is introduced as the origin of motivated behaviour. The functionalities mentioned above define the inputs received by the decisionmaking system for adapting its behaviour. Our decision-making system has been designed for being integrated into different robotic platforms due to its flexibility and modularity. Finally, we carried out several experiments to evaluate the architecture’s functionalities during real human-robot interaction scenarios. In these experiments, we assessed: ‱ How to endow social robots with adaptive affective mechanisms to overcome interaction limitations. ‱ Active user profiling using face recognition and human-robot interaction. ‱ A Preference Learning System we designed to predict and adapt the user preferences towards the robot’s entertainment activities for adapting the interaction. ‱ A Behaviour-based Reinforcement Learning System that allows the robot to learn the effects of its actions to behave appropriately in each situation. ‱ The biologically inspired robot behaviour using emulated biological processes and how the robot creates social bonds with each user. ‱ The robot’s expressiveness in affect (emotion and mood) and autonomic functions such as heart rate or blinking frequency.Programa de Doctorado en IngenierĂ­a ElĂ©ctrica, ElectrĂłnica y AutomĂĄtica por la Universidad Carlos III de MadridPresidente: Richard J. Duro FernĂĄndez.- Secretaria: ConcepciĂłn Alicia Monje Micharet.- Vocal: Silvia Ross

    Hedonic Quality or Reward? A Study of Basic Pleasure in Homeostasis and Decision Making of a Motivated Autonomous Robot

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    © The Author (s) 2016. Published by SAGE. This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).We present a robot architecture and experiments to investigate some of the roles that pleasure plays in the decision making (action selection) process of an autonomous robot that must survive in its environment. We have conducted three sets of experiments to assess the effect of different types of pleasure---related versus unrelated to the satisfaction of physiological needs---under different environmental circumstances. Our results indicate that pleasure, including pleasure unrelated to need satisfaction, has value for homeostatic management in terms of improved viability and increased flexibility in adaptive behavior.Peer reviewedFinal Published versio

    Emotion in Future Intelligent Machines

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    Over the past decades, research in cognitive and affective neuroscience has emphasized that emotion is crucial for human intelligence and in fact inseparable from cognition. Concurrently, there has been a significantly growing interest in simulating and modeling emotion in robots and artificial agents. Yet, existing models of emotion and their integration in cognitive architectures remain quite limited and frequently disconnected from neuroscientific evidence. We argue that a stronger integration of emotion in robot models is critical for the design of intelligent machines capable of tackling real world problems. Drawing from current neuroscientific knowledge, we provide a set of guidelines for future research in artificial emotion and intelligent machines more generally

    Computational animal welfare: Towards cognitive architecture models of animal sentience, emotion and wellbeing

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    To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.publishedVersio

    DSAAR: distributed software architecture for autonomous robots

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    Dissertação apresentada na Faculdade de CiĂȘncias e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia ElectrotĂ©cnicaThis dissertation presents a software architecture called the Distributed Software Architecture for Autonomous Robots (DSAAR), which is designed to provide the fast development and prototyping of multi-robot systems. The DSAAR building blocks allow engineers to focus on the behavioural model of robots and collectives. This architecture is of special interest in domains where several human, robot, and software agents have to interact continuously. Thus, fast prototyping and reusability is a must. DSAAR tries to cope with these requirements towards an advanced solution to the n-humans and m-robots problem with a set of design good practices and development tools. This dissertation will also focus on Human-Robot Interaction, mainly on the subject of teleoperation. In teleoperation human judgement is an integral part of the process, heavily influenced by the telemetry data received from the remote environment. So the speed in which commands are given and the telemetry data is received, is of crucial importance. Using the DSAAR architecture a teleoperation approach is proposed. This approach was designed to provide all entities present in the network a shared reality, where every entity is an information source in an approach similar to the distributed blackboard. This solution was designed to accomplish a real time response, as well as, the completest perception of the robots’ surroundings. Experimental results obtained with the physical robot suggest that the system is able to guarantee a close interaction between users and robot

    Emotions, Motivation-Based Action Selection and Dynamic Environments

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    In contrast to traditional approaches, where the focus is on developing or evolving artificial “brains” as the route to artificial intelligence (AI) more recent approaches have increasingly emphasised and modelled the role of “bodies” and “environments”. In turn, this has further encouraged ideas regarding aspects of intelligence as being best thought of as distributed across agent brains, bodies and environments. That is, as system properties emerging from interactions of these components. Action selection is commonly recognised as one of the problems all agents, whether biological or artificial, must face: deciding at any given moment “what to do next”. Researchers have generated many different action selection mechanisms as “solutions” to this problem. However, in the work of this thesis, we focus on one which takes its inspiration from biological ideas about the role and possible neural substrates of emotion. We use this to consider how models of brain-body-environment interactions might be more useful for the study of emotion, as well as action selection mechanisms. For, despite the many mechanisms proposed, the literature still lacks systematic ways to analyse their performance in combination with different physical and/or perceptual capabilities. That is, factors relating more directly to agent embodiment. In this thesis we have studied the performance of our selected architecture in a robotic predator-prey scenario known as the Hazardous Three Resource Problem. The predator-prey relationship is popular in artificial intelligence, both as an action selection problem and a situation which enables study of agent-agent interactions. Predators can act as catalysts for the evolution of prey agents in a “survival of the fittest” sense while, in their turn, prey agents are tests of predator ingenuity. For us, however, it is also a situation where emotion might naturally be assumed to have useful functions. To study action selection, emotion and brain-body-environment interactions in an artificial predator-prey relationship, we both advocate and adopt a bottom-up, animat approach. The animat approach to AI is one that emphasizes characteristics neglected by more traditional approaches. As such, it has embraced the study of robotic agents. One reason for this is the process of designing “real-world” agents forces us to consider practicalities simulations might not. What makes the use of robots particularly appealing for our work, however, is how it can give us a greater appreciation of more physical aspects of intelligence such as agent morphology and its integration with agent control mechanisms as well as environmental dynamics. Using LEGO robots, we show how the performance of our architecture varies in our chosen scenario with aspects of agent brain, body and environment. We argue our results complement existing research by contributing evidence from a real-world implementation, explicitly modelling ideas about action selection and emotion as distributed across, or best thought of as emerging from interactions between, agent brain, body and environment. In particular, this thesis shows how our selected architecture varies and benefits from further integration with aspects of agent “body”. It also acts as an example of an alternative form for the bottom-up development of artificial emotion, demonstrating wider applications for creating more adaptive action selection mechanisms. Comparing the robotic predator-prey relationships we have created to ethological evidence and theories, we argue our architecture may also have specific potential for future research and applications — having already proven itself capable of emerging multiple functions and propertie
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