11,482 research outputs found
Robots that Say âNoâ: Acquisition of Linguistic Behaviour in Interaction Games with Humans
Negation is a part of language that humans engage in pretty much from the onset of speech.
Negation appears at first glance to be harder to grasp than object or action labels, yet
this thesis explores how this family of âconceptsâ could be acquired in a meaningful way by
a humanoid robot based solely on the unconstrained dialogue with a human conversation
partner. The earliest forms of negation appear to be linked to the affective or motivational
state of the speaker. Therefore we developed a behavioural architecture which contains
a motivational system. This motivational system feeds its state simultaneously to other
subsystems for the purpose of symbol-grounding but also leads to the expression of the
robotâs motivational state via a facial display of emotions and motivationally congruent
body behaviours.
In order to achieve the grounding of negative words we will examine two different
mechanisms which provide an alternative to the established grounding via ostension with
or without joint attention. Two large experiments were conducted to test these two mechanisms.
One of these mechanisms is so called negative intent interpretation, the other one
is a combination of physical and linguistic prohibition. Both mechanisms have been described
in the literature on early child language development but have never been used in
human-robot-interaction for the purpose of symbol grounding.
As we will show, both mechanisms may operate simultaneously and we can exclude
none of them as potential ontogenetic origin of negation
Robots that Say âNoâ. Affective Symbol Grounding and the Case of Intent Interpretations
Š 2017 IEEE. This article has been accepted for publication in a forthcoming issue of IEEE Transactions on Cognitive and Developmental Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Modern theories on early child language acquisition tend to focus on referential words, mostly nouns, labeling concrete objects, or physical properties. In this experimental proof-of-concept study, we show how nonreferential negation words, typically belonging to a child's first ten words, may be acquired. A child-like humanoid robot is deployed in speech-wise unconstrained interaction with naïve human participants. In agreement with psycholinguistic observations, we corroborate the hypothesis that affect plays a pivotal role in the socially distributed acquisition process where the adept conversation partner provides linguistic interpretations of the affective displays of the less adept speaker. Negation words are prosodically salient within intent interpretations that are triggered by the learner's display of affect. From there they can be picked up and used by the budding language learner which may involve the grounding of these words in the very affective states that triggered them in the first place. The pragmatic analysis of the robot's linguistic performance indicates that the correct timing of negative utterances is essential for the listener to infer the meaning of otherwise ambiguous negative utterances. In order to assess the robot's performance thoroughly comparative data from psycholinguistic studies of parent-child dyads is needed highlighting the need for further interdisciplinary work.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.
⢠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
Interaction and Experience in Enactive Intelligence and Humanoid Robotics
We overview how sensorimotor experience can be operationalized for interaction scenarios in which humanoid robots acquire skills and linguistic behaviours via enacting a âform-of-lifeââ in interaction games (following Wittgenstein) with humans. The enactive paradigm is introduced which provides a powerful framework for the construction of complex adaptive systems, based on interaction, habit, and experience. Enactive cognitive architectures (following insights of Varela, Thompson and Rosch) that we have developed support social learning and robot ontogeny by harnessing information-theoretic methods and raw uninterpreted sensorimotor experience to scaffold the acquisition of behaviours. The success criterion here is validation by the robot engaging in ongoing human-robot interaction with naive participants who, over the course of iterated interactions, shape the robotâs behavioural and linguistic development. Engagement in such interaction exhibiting aspects of purposeful, habitual recurring structure evidences the developed capability of the humanoid to enact language and interaction games as a successful participant
A Robot Model of OC-Spectrum Disorders : Design Framework, Implementation and First Experiments
Š 2019 Massachusetts Institute of TechnologyComputational psychiatry is increasingly establishing itself as valuable discipline for understanding human mental disorders. However, robot models and their potential for investigating embodied and contextual aspects of mental health have been, to date, largely unexplored. In this paper, we present an initial robot model of obsessive-compulsive (OC) spectrum disorders based on an embodied motivation-based control architecture for decision making in autonomous robots. The OC family of conditions is chiefly characterized by obsessions (recurrent, invasive thoughts) and/or compulsions (an urge to carry out certain repetitive or ritualized behaviors). The design of our robot model follows and illustrates a general design framework that we have proposed to ground research in robot models of mental disorders, and to link it with existing methodologies in psychiatry, and notably in the design of animal models. To test and validate our model, we present and discuss initial experiments, results and quantitative and qualitative analysis regarding the compulsive and obsessive elements of OC-spectrum disorders. While this initial stage of development only models basic elements of such disorders, our results already shed light on aspects of the underlying theoretical model that are not obvious simply from consideration of the model.Peer reviewe
Behavior-Based Early Language Development on a Humanoid Robot
We are exploring the idea that early language acquisition could be better modelled on an artifcial creature by considering the pragmatic aspect of natural language and of its development in human infants. We have implemented a system of vocal behaviors on Kismet in which "words" or concepts are behaviors in a competitive hierarchy. This paper reports on the framework, the vocal system's architecture and algorithms, and some preliminary results from vocal label learning and concept formation
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