588 research outputs found
A flexible component-based robot control architecture for hormonal modulation of behaviour and affect
This document is the Accepted Manuscritpt of a paper published in Proceedings of 18th Annual Conference, TAROS 2017, Guildford, UK, July 19â21, 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
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 âSocial Bufferingâ in Artificial Social Agents: Contextual Affective Perception Matters
© 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 âstressâ 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 âsocial bufferingâ 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 agentâs long-term stress tolerance. These effects likely facilitate an agentâs 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 âsocial bufferingâ 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 âsocial bufferingâ 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 âbiomarkersâ of social support and environmental stress. For socially-situated artificial agents, we suggest that these âsocial bufferingâ 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
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 systemâs 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 robotâs
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 robotâs decisions also depend on what
the robot perceives from the environment, planned events listed in the robotâs agenda, and
the unique features of the user interacting with the robot.
The robotâs 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.
âą The robot incorporates user profiling methods to continuously gather information
from the user using direct and indirect feedback methods.
âą The robot has a Preference Learning System that predicts and adjusts the userâs
preferences to the robotâs activities during the interaction.
âą 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 architectureâs functionalities
during real human-robot interaction scenarios. In these experiments, we assessed:
âą How to endow social robots with adaptive affective mechanisms to overcome
interaction limitations.
âą Active user profiling using face recognition and human-robot interaction.
âą A Preference Learning System we designed to predict and adapt the user
preferences towards the robotâs entertainment activities for adapting the interaction.
âą A Behaviour-based Reinforcement Learning System that allows the robot to learn
the effects of its actions to behave appropriately in each situation.
âą The biologically inspired robot behaviour using emulated biological processes and
how the robot creates social bonds with each user.
âą The robotâs 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
© 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
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
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
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 robotsâ 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
In contrast to traditional approaches, where the focus is on developing or
evolving artificial âbrainsâ as the route to artificial intelligence (AI) more
recent approaches have increasingly emphasised and modelled the role of
âbodiesâ and âenvironmentsâ. 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 âwhat to do
nextâ. Researchers have generated many different action selection mechanisms
as âsolutionsâ 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 âsurvival of the
fittestâ 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 âreal-worldâ 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 âbodyâ. 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 â having already
proven itself capable of emerging multiple functions and propertie
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