162 research outputs found

    From Visuo-Motor Development to Low-level Imitation

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    We present the first stages of the developmental course of a robot using vision and a 5 degree of freedom robotic arm. During an exploratory behavior, the robot learns visuo-motor control of its mechanical arm. We show how a simple neural network architecture, combining elementary vision, a self-organized algorithm, and dynamical Neural Fields is able to learn and use proper associations between vision and arm movements, even if the problem is ill posed (2-D toward 3-D mapping and also mechanical redundancy between different joints). Highlighting the generic aspect of such an architecture, we show as a robotic result that it is used as a basis for simple gestural imitations of humans. Finally we show how the imitative mechanism carries on the developmental course, allowing the acquisition of more and more complex behavioral capabilities

    Development of Cognitive Capabilities in Humanoid Robots

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    Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate grand challenge for science and technology in general, and especially for the computational intelligence community. Recent theories in autonomous cognitive systems have focused on the close integration (grounding) of communication with perception, categorisation and action. Cognitive systems are essential for integrated multi-platform systems that are capable of sensing and communicating. This thesis presents a cognitive system for a humanoid robot that integrates abilities such as object detection and recognition, which are merged with natural language understanding and refined motor controls. The work includes three studies; (1) the use of generic manipulation of objects using the NMFT algorithm, by successfully testing the extension of the NMFT to control robot behaviour; (2) a study of the development of a robotic simulator; (3) robotic simulation experiments showing that a humanoid robot is able to acquire complex behavioural, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to cooperate with human users, and to adapt its abilities to changes in internal and environmental conditions. The model and the experimental results reported in this thesis, emphasise the importance of embodied cognition, i.e. the humanoid robot's physical interaction between its body and the environment

    Understanding the embodied teacher : nonverbal cues for sociable robot learning

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (p. 103-107).As robots enter the social environments of our workplaces and homes, it will be important for them to be able to learn from natural human teaching behavior. My research seeks to identify simple, non-verbal cues that human teachers naturally provide that are useful for directing the attention of robot learners. I conducted two novel studies that examined the use of embodied cues in human task learning and teaching behavior. These studies motivated the creation of a novel data-gathering system for capturing teaching and learning interactions at very high spatial and temporal resolutions. Through the studies, I observed a number of salient attention-direction cues, the most promising of which were visual perspective, action timing, and spatial scaffolding. In particular, this thesis argues that spatial scaffolding, in which teachers use their bodies to spatially structure the learning environment to direct the attention of the learner, is a highly valuable cue for robotic learning systems. I constructed a number of learning algorithms to evaluate the utility of the identified cues. I situated these learning algorithms within a large architecture for robot cognition, augmented with novel mechanisms for social attention and visual perspective taking. Finally, I evaluated the performance of these learning algorithms in comparison to human learning data, providing quantitative evidence for the utility of the identified cues. As a secondary contribution, this evaluation process supported the construction of a number of demonstrations of the humanoid robot Leonardo learning in novel ways from natural human teaching behavior.by Matthew Roberts Berlin.Ph.D

    Framework of active robot learning

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    A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Master of Science by researchIn recent years, cognitive robots have become an attractive research area of Artificial Intelligent (AI). High-order beliefs for cognitive robots regard the robots' thought about their users' intention and preference. The existing approaches to the development of such beliefs through machine learning rely on particular social cues or specifically defined award functions . Therefore, their applications can be limited. This study carried out primary research on active robot learning (ARL) which facilitates a robot to develop high-order beliefs by actively collecting/discovering evidence it needs. The emphasis is on active learning, but not teaching. Hence, social cues and award functions are not necessary. In this study, the framework of ARL was developed. Fuzzy logic was employed in the framework for controlling robot and for identifying high-order beliefs. A simulation environment was set up where a human and a cognitive robot were modelled using MATLAB, and ARL was implemented through simulation. Simulations were also performed in this study where the human and the robot tried to jointly lift a stick and keep the stick level. The simulation results show that under the framework a robot is able to discover the evidence it needs to confirm its user's intention

    09341 Abstracts Collection -- Cognition, Control and Learning for Robot Manipulation in Human Environments

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    From 16.08. to 21.08.2009, the Dagstuhl Seminar 09341 ``Cognition, Control and Learning for Robot Manipulation in Human Environments \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

    Teachers’ Perception for Integrating Educational Robots and Use as Teaching Assistants in Thai Primary Schools

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    This study focused on teachers’ perception for integrating educational robots into learning and the feedback of teachers and students who used an education robot prototype as a part of learning. Data were collected from 510 primary school teachers who were used in a confirmatory analysis of factor model. Confirmatory Factor Analysis (CFA) indicated a good fit with a six-factor model in the observed data, which could be presented through six dimensions of robotic education quality, including social interaction, cognitive function, teaching method, learner characteristics, main features, and content. The prototype-testing phase was carried out using 5th grade students at a primary school in Thailand. The robot was tested for 10 hours, which included 20 students participating in the focus group. The research results showed that integrating the six dimensions of robotic education into the educational robot prototype resulted in a strong positive improvement in the focus groups learners’ behavior and supported the instructors during the learning process. In contrast, some teachers lacked experience and confidence with robots integrated with LMS, which caused challenging obstacles in teaching. The results were achieved when integrating the six-factor model into education robots to improve student learning. Future researchers should expand their studies to look into the opportunities and challenges that teachers and school administrators face in the classroom. Doi: 10.28991/esj-2021-SP1-09 Full Text: PD

    On the Possibility of Robots Having Emotions

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    I argue against the commonly held intuition that robots and virtual agents will never have emotions by contending robots can have emotions in a sense that is functionally similar to humans, even if the robots\u27 emotions are not exactly equivalent to those of humans. To establish a foundation for assessing the robots\u27 emotional capacities, I first define what emotions are by characterizing the components of emotion consistent across emotion theories. Second, I dissect the affective-cognitive architecture of MIT\u27s Kismet and Leonardo, two robots explicitly designed to express emotions and to interact with humans, in order to explore whether they have emotions. I argue that, although Kismet and Leonardo lack the subjective feelings component of emotion, they are capable of having emotions
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