6,604 research outputs found
Human mental models of humanoid robots
Effective communication between a person and a robot may depend on whether there exists a common ground of understanding between the two. In two experiments modelled after human-human studies we examined how people form a mental model of a robot’s factual knowledge. Participants estimated the robot’s knowledge by extrapolating from their own knowledge and from information about the robot’s origin and language. These results suggest that designers of humanoid robots must attend not only to the social cues that robots emit but also to the information people use to create mental models of a robot.published_or_final_versio
Human Mental Models of Humanoid Robots
Effective communication between a person and a robot may depend on whether there exists a common ground of understanding between the two. In two experiments modelled after human-human studies we examined how people form a mental model of a robot’s factual knowledge. Participants estimated the robot’s knowledge by extrapolating from their own knowledge and from information about the robot’s origin and language. These results suggest that designers of humanoid robots must attend not only to the social cues that robots emit but also to the information people use to create mental models of a robot.published_or_final_versio
Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics
Developmental robotics is an emerging field located
at the intersection of developmental psychology
and robotics, that has lately attracted
quite some attention. This paper gives a survey of
a variety of research projects dealing with or inspired
by developmental issues, and outlines possible
future directions
The ITALK project : A developmental robotics approach to the study of individual, social, and linguistic learning
This is the peer reviewed version of the following article: Frank Broz et al, “The ITALK Project: A Developmental Robotics Approach to the Study of Individual, Social, and Linguistic Learning”, Topics in Cognitive Science, Vol 6(3): 534-544, June 2014, which has been published in final form at doi: http://dx.doi.org/10.1111/tops.12099 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." Copyright © 2014 Cognitive Science Society, Inc.This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.Peer reviewe
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
Developmental psychology and neuroimaging
research identified a close link between numbers and fingers,
which can boost the initial number knowledge in children. Recent
evidence shows that a simulation of the children's embodied
strategies can improve the machine intelligence too. This article
explores the application of embodied strategies to convolutional
neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually
acquired while operating rather than being abundant and fully
available as the classical machine learning scenarios. The
experimental analyses show that the proprioceptive information
from the robot fingers can improve network accuracy in the
recognition of handwritten Arabic digits when training examples
and epochs are few. This result is comparable to brain imaging
and longitudinal studies with young children. In conclusion, these
findings also support the relevance of the embodiment in the case
of artificial agents’ training and show a possible way for the
humanization of the learning process, where the robotic body can
express the internal processes of artificial intelligence making it
more understandable for humans
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
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