278 research outputs found
Real-time generation and adaptation of social companion robot behaviors
Social robots will be part of our future homes.
They will assist us in everyday tasks, entertain us, and provide helpful advice.
However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate.
An essential skill of every social robot is verbal and non-verbal communication.
In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine.
Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors.
In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot.
However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems.
This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences.
Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence.
The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning.
Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning.
It provides a higher-level view from the system designer's perspective and guidance from the start to the end.
It illustrates the process of modeling, simulating, and evaluating such adaptation processes.
Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness.
The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes.
They are evaluated in the lab and in in-situ studies.
These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor.
Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukĂźnftigen Zuhauses sein.
Sie werden uns bei alltäglichen Aufgaben unterstßtzen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen.
Noch gibt es allerdings technische Herausforderungen, die zunächst ßberwunden werden mßssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen.
Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation.
Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine VerkĂśrperung, die Erwartungen an die Maschine weckt.
Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natĂźrliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen.
DarĂźber hinaus mĂźssen Roboter auch die individuellen Vorlieben der Benutzer berĂźcksichtigen.
So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter fßhrt.
Roboter haben jedoch keine menschliche Intuition - sie mĂźssen mit entsprechenden Algorithmen fĂźr diese Probleme ausgestattet werden.
In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedßrfnisse und Vorlieben des Benutzers anzupassen.
Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern.
Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle ßber die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis fßr maschinelles Lernen.
Daher wird in dieser Arbeit ein konzeptioneller Rahmen fßr die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt.
Er bietet eine Ăźbergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende.
Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse.
Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten.
Der konzeptionelle Rahmen wird fßr mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen fßhrt, die in Labor- und In-situ-Studien evaluiert werden.
Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der PersÜnlichkeit, dem Persona, der HÜflichkeit und dem Humor des Roboters liegt.
In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an
A philosophical approach to satire and humour in social context
The topic of my dissertation is satire. This seems to excite many people, and over the past four years I have heard many variations of a similar refrain: âOh, wow. Youâre studying satire? Thatâs very topical. You must have a lot of material to work with.â There is a way in which this is true, though I suspect in a way that diverges from the way that most of my interlocutors believed. I suspect that the material they imagined me to be working with was the output of Donald Trump, first a candidate for and now holder of the office of President of the United States. That is not the material I find interesting. More interesting to me is their statement. It evinces a number of beliefs that I find interesting, chief among them that Trump makes the current period particularly apt for satire, as if a doddery and incompetent ruling class is somehow a recent phenomenon. And I suspect that underneath this belief in the aptness of satire is a belief in the power of satire. Satire is somehow how people are going to strike back at the vice-governed fools who rule us. Those beliefs are the material I want to work with.
This is my interest in satire, then: not so much what it is about satire that makes it powerful, but what is it about satire that makes people think it is powerful? And what is it about satire that makes people think it is powerful when there is pretty powerful evidence that it is not? Unfortunately, here is where I run into the problem that in a very important way I do not have a lot of material to work with. There is not an awful lot on satire within the analytic tradition: outside of a few references to satire, analytic discussions of satire are limited to two short articles. Accordingly, I have taken the task of my dissertation to be to create an account of satire that helps to bring forward why satire is a thing that people can imagine to be powerful, that they can imagine to be politically effective.
My dissertation will effectively have two halves, one where I build my account and one where I begin to apply it. The purpose of applying my account, which will comprise the final two chapters, will not be to show its implications so much as how it can be used. My goal will not so much be to give definitive answers about the nature of satire, but rather to give a demonstration of how my account facilitates engaging questions about the role of satire in social and political context
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.
⢠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 userâs
preferences to the robotâs 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 architectureâs 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 robotâs 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 robotâs expressiveness in affect (emotion and mood) and autonomic functions
such as heart rate or blinking frequency.Programa de Doctorado en IngenierĂa ElĂŠctrica, ElectrĂłnica y AutomĂĄtica por la Universidad Carlos III de MadridPresidente: Richard J. Duro FernĂĄndez.- Secretaria: ConcepciĂłn Alicia Monje Micharet.- Vocal: Silvia Ross
"Continuing to Play": Humour in Ralph Ellison's Invisible Man
Ralph Ellisonâs novel Invisible Man (1952) is sometimes comical, despite many criticsâ claims
of the opposite. This masterâs thesis provides a close reading of humorous elements in the novel,
demonstrating how humorous framings of sometimes tragic realities of black Americans can be
effective to engage readersâ reflections. The aims are to analyse 1) some historical and
psychological contexts behind these humorous scenes, and 2) Ellisonâs use of humour as a
means to critique and question commonly held assumptions about race, and about both
individual and political possibilities and limitations. The Collected Essays of Ralph Ellison
(1994) provide many insightful ideas from Ellison that informs this analysis.
The main argument is that, by using humour, Ellison implicitly asks readers to look
beneath the surface. Readers may laugh at the otherwise unlaughable, and in a disengaged way
reflect upon the underlying meanings, and see the invisibles. In a racially segregated USA
during the 1930s, the naĂŻve, young black protagonist believes that his self-worth depends upon
white menâs judgments. This puts him into situational ironies where his misinterpretations
sometimes become comical. Ellison satirises liberalismâs tradition of paternalism,
communismâs blind insistence upon conformity and discipline, and black nationalismâs
destructive hatred, and shows what hides behind the masked façades. Moreover, Ellison
overturns racial stereotypes by portraying black individuals as witty, eloquent, and autonomous.
He incorporates African American humour like signifying and playing the dozens, together
with folklore, jazz, and the blues. Ellisonâs irony plays with different interpretations of laws,
political action, and personal responsibility. In other words, Ellison eloquently incorporates
many different forms of humour which may appeal to different kinds of readers, in his vision
that accepts a pluralistic and diverse America
Machine Medical Ethics
In medical settings, machines are in close proximity with human beings: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. Machines in these contexts are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy.
As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for empathy and emotion detection necessary? What about consciousness?
The essays in this collection by researchers from both humanities and science describe various theoretical and experimental approaches to adding medical ethics to a machine, what design features are necessary in order to achieve this, philosophical and practical questions concerning justice, rights, decision-making and responsibility, and accurately modeling essential physician-machine-patient relationships.
This collection is the first book to address these 21st-century concerns
Imitating Human Responses via a Dual-Process Model Approach
Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels the intuitive mode as âSystem 1â and the reflective mode as âSystem 2â. The current research suggests by leveraging an agent which forms decisions based on a dual-process model, an agent in a human-machine team can maintain a better shared mental model with the user. Evaluation of DPM-MN in a game called Space Navigator shows that DPM-MN presents a successful dual-process theory motivated model
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