871 research outputs found
Bringing Human Robot Interaction towards _Trust and Social Engineering
Robots started their journey in books and movies; nowadays, they are becoming an
important part of our daily lives: from industrial robots, passing through entertainment
robots, and reaching social robotics in fields like healthcare or education.
An important aspect of social robotics is the human counterpart, therefore, there is
an interaction between the humans and robots. Interactions among humans are often
taken for granted as, since children, we learn how to interact with each other. In robotics,
this interaction is still very immature, however, critical for a successful incorporation of
robots in society. Human robot interaction (HRI) is the domain that works on improving
these interactions.
HRI encloses many aspects, and a significant one is trust. Trust is the assumption that
somebody or something is good and reliable; and it is critical for a developed society.
Therefore, in a society where robots can part, the trust they could generate will be essential
for cohabitation.
A downside of trust is overtrusting an entity; in other words, an insufficient alignment
of the projected trust and the expectations of a morally correct behaviour. This effect
could negatively influence and damage the interactions between agents. In the case of
humans, it is usually exploited by scammers, conmen or social engineers - who take
advantage of the people's overtrust in order to manipulate them into performing actions
that may not be beneficial for the victims.
This thesis tries to shed light on the development of trust towards robots, how this
trust could become overtrust and be exploited by social engineering techniques. More
precisely, the following experiments have been carried out: (i) Treasure Hunt, in which
the robot followed a social engineering framework where it gathered personal
information from the participants, improved the trust and rapport with them, and at the
end, it exploited that trust manipulating participants into performing a risky action.
(ii) Wicked Professor, in which a very human-like robot tried to enforce its authority to
make participants obey socially inappropriate requests. Most of the participants realized
that the requests were morally wrong, but eventually, they succumbed to the robot'sauthority while holding the robot as morally responsible. (iii) Detective iCub, in which it
was evaluated whether the robot could be endowed with the ability to detect when the
human partner was lying. Deception detection is an essential skill for social engineers and
professionals in the domain of education, healthcare and security. The robot achieved
75% of accuracy in the lie detection. There were also found slight differences in the
behaviour exhibited by the participants when interacting with a human or a robot
interrogator.
Lastly, this thesis approaches the topic of privacy - a fundamental human value. With
the integration of robotics and technology in our society, privacy will be affected in ways
we are not used. Robots have sensors able to record and gather all kind of data, and it is
possible that this information is transmitted via internet without the knowledge of the
user. This is an important aspect to consider since a violation in privacy can heavily
impact the trust.
Summarizing, this thesis shows that robots are able to establish and improve trust
during an interaction, to take advantage of overtrust and to misuse it by applying different
types of social engineering techniques, such as manipulation and authority. Moreover,
robots can be enabled to pick up different human cues to detect deception, which can
help both, social engineers and professionals in the human sector. Nevertheless, it is of
the utmost importance to make roboticists, programmers, entrepreneurs, lawyers,
psychologists, and other sectors involved, aware that social robots can be highly beneficial
for humans, but they could also be exploited for malicious purposes
The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling
Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the ïżœexperimenterïżœ, and Mary, the ïżœcomputational modellerïżœ. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling
Persuasiveness of social robot âNaoâ based on gaze and proximity
Social Robots have widely infiltrated the retail and public space. Mainly, social robots are being utilized across a wide range of scenarios to influence decision making, disseminate information, and act as a signage mechanism, under the umbrella of Persuasive Robots or Persuasive Technology. While there have been several studies in the afore-mentioned area, the effect of non-verbal behaviour on persuasive abilities is generally unexplored. Therefore, in this research, we report whether two key non-verbal attributes, namely proximity and gaze, can elicit persuasively, compliance, and specific personality appeals. For this, we conducted a 2 (eye gaze) x 2 (proximity) between-subjects experiment where participants viewed a video-based scenario of the Nao robot. Our initial results did not reveal any significant results based on the non-verbal attributes. However, perceived compliance and persuasion were significantly correlated with knowledge, responsiveness, and trustworthiness. In conclusion, we discuss how the design of a robot could make it more convincing as extensive marketing and brand promotion companies could use robots to enhance their advertisement operations
Human-Machine Communication: Complete Volume. Volume 3. Diffusion of Human-Machine Communication During and After the COVID-19 Pandemic
This is the complete volume of HMC Volume 3. Diffusion of Human-Machine Communication During and After the COVID-19 Pandemi
A Value-Sensitive Design Approach to Intelligent Agents
This chapter proposed a novel design methodology called Value-Sensitive Design and its potential application to the field of artificial intelligence research and design. It discusses the imperatives in adopting a design philosophy that embeds values into the design of artificial agents at the early stages of AI development. Because of the high risk stakes in the unmitigated design of artificial agents, this chapter proposes that even though VSD may turn out to be a less-than-optimal design methodology, it currently provides a framework that has the potential to embed stakeholder values and incorporate current design methods. The reader should begin to take away the importance of a proactive design approach to intelligent agents
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 zukĂŒnftigen Zuhauses sein.
Sie werden uns bei alltĂ€glichen Aufgaben unterstĂŒtzen, uns unterhalten und uns mit hilfreichen RatschlĂ€gen versorgen.
Noch gibt es allerdings technische Herausforderungen, die zunĂ€chst ĂŒberwunden werden mĂŒssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen.
Eine wesentliche FĂ€higkeit 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 Verkörperung, die Erwartungen an die Maschine weckt.
Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natĂŒrliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen.
DarĂŒber hinaus mĂŒssen Roboter auch die individuellen Vorlieben der Benutzer berĂŒcksichtigen.
So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprĂ€gt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter fĂŒhrt.
Roboter haben jedoch keine menschliche Intuition - sie mĂŒssen mit entsprechenden Algorithmen fĂŒr diese Probleme ausgestattet werden.
In dieser Arbeit wird der Einsatz von bestĂ€rkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die BedĂŒrfnisse 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 ĂŒber die multimodale Verhaltenserzeugung des Roboters, ein VerstĂ€ndnis des menschlichen Feedbacks und eine algorithmische Basis fĂŒr maschinelles Lernen.
Daher wird in dieser Arbeit ein konzeptioneller Rahmen fĂŒr die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestĂ€rkendem Lernen entwickelt.
Er bietet eine ĂŒbergeordnete 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 fĂŒr mehrere AnwendungsfĂ€lle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen fĂŒhrt, die in Labor- und In-situ-Studien evaluiert werden.
Diese AnsÀtze befassen sich mit typischen AktivitÀten in hÀuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit 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
Addressing joint action challenges in HRI: Insights from psychology and philosophy
The vast expansion of research in human-robot interactions (HRI) these last decades has been accompanied by
the design of increasingly skilled robots for engaging in joint actions with humans. However, these advances
have encountered significant challenges to ensure fluent interactions and sustain human motivation through the
different steps of joint action. After exploring current literature on joint action in HRI, leading to a more precise
definition of these challenges, the present article proposes some perspectives borrowed from psychology and
philosophy showing the key role of communication in human interactions. From mutual recognition between
individuals to the expression of commitment and social expectations, we argue that communicative cues can
facilitate coordination, prediction, and motivation in the context of joint action. The description of several notions
thus suggests that some communicative capacities can be implemented in the context of joint action for
HRI, leading to an integrated perspective of robotic communication.French National Research Agency (ANR) ANR-16-CE33-0017
ANR-17-EURE-0017 FrontCog
ANR-10-IDEX-0001-02 PSLJuan de la Cierva-Incorporacion grant IJC2019-040199-ISpanish Government PID2019-108870GB-I00
PID2019-109764RB-I0
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