6,036 research outputs found
Rational imitation for robots: the cost difference model
© 2017, © The Author(s) 2017. Infants imitate behaviour flexibly. Depending on the circumstances, they copy both actions and their effects or only reproduce the demonstratorâs intended goals. In view of this selective imitation, infants have been called rational imitators. The ability to selectively and adaptively imitate behaviour would be a beneficial capacity for robots. Indeed, selecting what to imitate is an outstanding unsolved problem in the field of robotic imitation. In this paper, we first present a formalized model of rational imitation suited for robotic applications. Next, we test and demonstrate it using two humanoid robots
What should a robot learn from an infant? Mechanisms of action interpretation and observational learning in infancy
The paper provides a summary of our
recent research on preverbal infants (using
violation-of-expectation and observational
learning paradigms) demonstrating that one-year-olds interpret and draw systematic
inferences about otherâs goal-directed actions,
and can rely on such inferences when imitating
otherâs actions or emulating their goals. To
account for these findings it is proposed that one-year-olds apply a non-mentalistic action
interpretational system, the âteleological stanceâ
that represents actions by relating relevant
aspects of reality (action, goal-state, and
situational constraints) through the principle of
rational action, which assumes that actions
function to realize goal-states by the most
efficient means available in the actorâs situation.
The relevance of these research findings and the
proposed theoretical model for how to realize the
goal of epigenetic robotics of building a âsocially
relevantâ humanoid robot is discussed
Introduction: The Third International Conference on Epigenetic Robotics
This paper summarizes the paper and poster contributions
to the Third International Workshop on
Epigenetic Robotics. The focus of this workshop is
on the cross-disciplinary interaction of developmental
psychology and robotics. Namely, the general
goal in this area is to create robotic models of the
psychological development of various behaviors. The
term "epigenetic" is used in much the same sense as
the term "developmental" and while we could call
our topic "developmental robotics", developmental
robotics can be seen as having a broader interdisciplinary
emphasis. Our focus in this workshop is
on the interaction of developmental psychology and
robotics and we use the phrase "epigenetic robotics"
to capture this focus
Proposal for an Approach to Artificial Consciousness Based on Self-Consciousness
Current research on artificial consciousness is focused on\ud
phenomenal consciousness and on functional consciousness.\ud
We propose to shift the focus to self-consciousness in order\ud
to open new areas of investigation. We use an existing\ud
scenario where self-consciousness is considered as the result of an evolution of representations. Application of the scenario to the possible build up of a conscious robot also introduces questions relative to emotions in robots. Areas of investigation are proposed as a continuation of this approach
Prediction of intent in robotics and multi-agent systems.
Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches
'Obsessed with goals': functions and mechanisms of teleological interpretation of actions in humans
Humans show a strong and early inclination to interpret observed behaviours of others as goal-directed actions. We identify two main epistemic functions that this âteleological obsessionâ serves: on-line prediction and social learning. We show how teleological action interpretations can serve these functions by drawing on two kinds of inference (âaction-to-goalâ or âgoal-to-actionâ), and argue that both types of teleological inference constitute inverse problems that can only be solved by further assumptions. We pinpoint the assumptions that the three currently proposed mechanisms of goal attribution (action-effect associations, simulation procedures, and teleological reasoning) imply, and contrast them with the functions they are supposed to fulfil. We argue that while action-effect associations and simulation procedures are generally well suited to serve on-line action monitoring and prediction, social learning of new means actions and artefact functions requires the inferential productivity of teleological reasoning
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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