Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

195 results found

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.

Conference paper

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.

Journal article

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, 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.

Conference paper

Prince CG, Demiris Y, 2003, Editorial: Introduction to the special issue on epigenetic robotics, Adaptive Behaviour, Vol: 11, Pages: 75-77, ISSN: 1059-7123

Journal article

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 chapter

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.

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

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

Book

, 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.

Conference paper

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.

Journal article

DEMIRIS J, 1994, EXPERIMENTS TOWARDS ROBOTIC LEARNING BY IMITATION, 12th National Conference on Artificial Intelligence, Publisher: M I T PRESS, Pages: 1439-1439

Conference paper

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