Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • Conference paper
    Simmons G, Demiris Y, 2004,

    Biologically inspired optimal robot arm control with signal-dependent noise

    , IEEE/RSJ International Conference on Intelligent Robots and Systems, Pages: 491-496

    Progress in the field of humanoid robotics and the need to find simpler ways to program such robots has prompted research into computational models for robotic learning from human demonstration. To further investigate biologically inspired human-like robotic movement and imitation, we have constructed a framework based on three key features of human movement and planning: optimality, modularity and learning. In this paper we focus on the application of optimality principles to the production of human-like movement by a robot arm. Among computational theories of human movement, the signal-dependent noise, or minimum variance, model was chosen as a biologically realistic control scheme to produce human-like movement. A well known optimal control algorithm, the linear quadratic regulator, was adapted to implement this model. The scheme was applied both in simulation and on a real robot arm, which demonstrated human-like movement profiles in a point-to-point reaching experiment.

  • Conference paper
    Johnson M, Demiris Y, 2004,

    Abstraction in Recognition to Solve the Correspondence Problem for Robot Imitation

    , Towards Autonomous Robotic Systems, TAROS 2004, Pages: 63-70

    A considerable part of the imitation problem is finding mechanisms that link the recognition of actions that are being demonstrated to the execution of the same actions by the imitator. In a situation where a human is instructing a robot, the problem is made more complicated by the difference in morphology. In this paper we present an imitation framework that allows a robot to recognise and imitate object-directed actions performed by a human demonstrator by solving the correspondence problem. The recognition is achieved using an abstraction mechanism that focuses on the features of the demonstration that are important to the imitator. The abstraction mechanism is applied to experimental scenarios in which a robot imitates human- demonstrated tasks of transporting objects be- tween tables.

  • Journal article
    Demiris Y, Johnson M, 2003,

    Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning

    , Connection Science, Vol: 15, Pages: 231-243, ISSN: 0954-0091

    One of the most important abilities for an agent's cognitive development in a social environment is the ability to recognize and imitate actions of others. In this paper we describe a cognitive architecture for action recognition and imitation, and present experiments demonstrating its implementation in robots. Inspired by neuroscientific and psychological data, and adopting a ‘simulation theory of mind’ approach, the architecture uses the motor systems of the imitator in a dual role, both for generating actions, and for understanding actions when performed by others. It consists of a distributed system of inverse and forward models that uses prediction accuracy as a means to classify demonstrated actions. The architecture is also shown to be capable of learning new composite actions from demonstration.

  • Conference paper
    Eneje E, Demiris Y, 2003,

    Towards Robot Intermodal Matching Using Spiking Neurons

    , IROS'03 Workshop on Programming by Demonstration, Pages: 95-99

    For a robot to successfully learn from demonstration it must posses the ability to reproduce the actions of a teacher. For this to happen, the robot must generate motor signals to match its proprioceptively perceived state with that of the visually perceived state of a teacher. In this paper we describe a real time matching model at a neural level of description. Experimental results from matching of arm movements, using dynamically simulated articulated robots, are presented.

  • Conference paper
    Johnson M, Demiris Y, Johnson MR, Demiris Yet al., 2003,

    An integrated rapid development environment for computer-aided robot design and simulation

    , Bury St Edmunds, International Conference on Mechatronics, ICOM 2003, Publisher: Wiley, Pages: 485-490

    We present our work towards the development of a rapid prototyping integrated environment for the design and dynamical simulation of multibody robotic systems. Subsequently, we demonstrate its current functionality in a case study involving the construction of a 130 DoF humanoid robot that attempts to closely match human motion capabilities. The modelling system relies exclusively on open-source software libraries thus offering high levels of customization and extensibility to the end-user.

  • Journal article
    Prince CG, Demiris Y, 2003,

    Editorial: Introduction to the special issue on epigenetic robotics

    , Adaptive Behaviour, Vol: 11, Pages: 75-77, ISSN: 1059-7123
  • Conference paper
    Demiris Y, 2002,

    Biologically inspired robot imitation mechanisms and their application as models of mirror neurons

    , Proceedings of EPSRC/BBSRC workshop on biologically inspired robotics, Pages: 126-133
  • Conference paper
    Demiris Y, 2002,

    Mirror neurons, imitation and the learning of movement sequences

    , Singapore, 9th international conference on neural information processing (ICONIP), Singapore, Singapore, 18 - 22 November 2002, Publisher: Nanyang Technological Univ, Pages: 111-115

    We draw inspiration from properties of "mirror" neurons discovered in the macaque monkey brain area F5, to design and implement a distributed behaviour-based architecture that equips robots with movement imitation abilities. We combine this generative route with a learning route, and demonstrate how new composite behaviours that exhibit mirror neuron like properties can be learned from demonstration.

  • Book chapter
    Demiris Y, Hayes G, 2002,

    Imitation as a dual-route process featuring predictive and learning components: a biologically plausible computational model

    , Imitation in animals and artifacts, Editors: Dautenhahn, Nehaniv, Cambridge, Massachussetts, Publisher: MIT Press, Pages: 327-361, ISBN: 9780262042031
  • Book
    Balkenius C, Prince C, Demiris Y, Marom Y, Kozima Het al., 2001,

    Proceedings of the first international workshop on epigenetic robotics: modeling cognitive development in robotic systems

    , Lund, Publisher: Lund University, ISBN: 9789163114656
  • Conference paper
    , 2000,

    Advances in Robot Learning, 8th European Workshop on Learning Robots, EWLR-8, Lausanne, Switzerland, September 18, 1999, Proceedings

    , Publisher: Springer
  • Conference paper
    , 1998,

    Learning Robots, 6th European Workshop, EWLR-6, Brighton, England, UK, August 1-2, 1997, Proceedings

    , Publisher: Springer
  • Conference paper
    Demiris Y, Hayes G, 1997,

    Do Robots Ape?

    , AAAI Fall Symposium on Socially Intelligent Agents, Publisher: AAAI, Pages: 28-30

    Within the context of two sets of robotic experiments we have performed, we examine some representational and algorithmic issues that need to be addressed in order to equip robots with the capacity to imitate. We suggest that some of the di culties might be eased by placing imitation architectures within a wider social context.

  • Journal article
    Klingspor V, Demiris Y, Kaiser M, 1997,

    Human Robot communication and Machine Learning

    , Applied Artificial Intelligence: an international journal, Vol: 11, Pages: 719-746
  • Journal article
    Klingspor V, Demiris Y, Kaiser M, 1997,

    Human Robot Communication and Machine Learning

    , Applied Artificial Intelligence, Vol: 11, Pages: 719-746

    Human-Robot Interaction and especially Human-Robot Communication (HRC) is of primary importance for the development of robots that operate outside production lines and cooperate with humans. In this paper, we review the state of the art and discuss two complementary aspects of the role machine learning plays in HRC. First, we show how communication itself can benefit from learning, e.g. by building human-understandable symbols from a robot’s perceptions and actions. Second, we investigate the power of non-verbal communication and imitation learning mechanisms for robot programming.

  • Conference paper
    DEMIRIS J, 1994,

    EXPERIMENTS TOWARDS ROBOTIC LEARNING BY IMITATION

    , 12th National Conference on Artificial Intelligence, Publisher: M I T PRESS, Pages: 1439-1439

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=559&limit=50&page=4&respub-action=search.html Current Millis: 1582623738325 Current Time: Tue Feb 25 09:42:18 GMT 2020