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

 

1011Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

203 results found

Khadhouri B, Demiris Y, 2005, Attention shifts during action sequence recognition for social robots, International Conference on Advanced Robotics, Publisher: IEEE, Pages: 468-475

Human action understanding is an important component of our research towards social robots that can operate among humans. A crucial element of this component is visual attention - where should a robot direct its limited visual and computational resources during the perception of a human action? In this paper, we propose a computational model of an attention mechanism that combines the saliency of top-down elements, based on multiple hypotheses about the demonstrated action, with the saliency of bottom up components. We implement our attention mechanism on a robot, and examine its performance during the observation of object-directed human actions. Furthermore, we propose a method for resetting this model that allows it to work on multiple behaviours observed in a sequence. We also implement and investigate this method's performance on the robot.

Conference paper

Johnson M, Demiris Y, 2005, Hierarchies of Coupled Inverse and Forward Models for Abstraction in Robot Action Planning, Recognition and Imitation, International Symposium on Imitation in Animals and Artifacts, Publisher: AISB, Pages: 69-76

Coupling internal inverse and forward models gives rise to on-line simulation processes that may be used as a common computational substrate for action execution, planning, recognition, imitation and learning. In this paper, multiple coupled internal inverse and forward models are arranged in a hierarchical fashion, with each level of the hierarchy interacting with other levels through top-down and bottom-up processes. Through experiments involving imitation of a human demonstrator performing object manipulation tasks, this architecture is shown to equip a robot with a multi-level motor abstraction capability. This is then used to solve the correspondence problem in action recognition. The architecture is inspired by biological evidence.

Conference paper

Demiris Y, Dautenhahn K, Nehaniv C, 2005, AISB'05: Social Intelligence and Interaction in animals, robots and agents: proceedings of the 3rd international symposium on imitation in animals and artifacts, University of Hertfordshire, Hatfield, UK, 12 - 15 April 2005, Publisher: SSAISB

Book

Khadhouri B, Demiris Y, 2005, Attention shifts during action sequence recognition for social robots, New York, 12th international conference on advanced robotics, 17 - 20 July 2005, Seattle, WA, Publisher: Ieee, Pages: 468-475

Conference paper

Simmons G, Demiris Y, 2004, Imitation of human demonstration using a biologically inspired modular optimal control scheme, New York, IEEE/RAS International Conference on Humanoid Robots, Publisher: IEEE, Pages: 215-234

Download Citation Email Print Request Permissions Save to ProjectProgress 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 describe a computational motor system, based on the minimum variance model of human movement, that uses optimality principles to produce human-like movement in a robot arm. Within this motor system different movements are represented in a modular structure. When the system observes a demonstrated movement, the motor system uses these modules to produce motor commands which are used to update an internal state representation. This is used so that the system can recognize known movements and move the robot arm accordingly, or extract key features from the demonstrated movement and use them to learn a new module. The active involvement of the motor system in the recognition and learning of observed movements has its theoretical basis in the direct matching hypothesis and the use of a model for human-like movement allows the system to learn from human demonstration.

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.

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

Mataric MJ, Williamson M, Demiris J, Mohan Aet al., 1998, Behavior-based primitives for articulated control, 5th International Conference on Simulation of Adaptive Behavior, Publisher: M I T PRESS, Pages: 165-170, ISSN: 1089-4365

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

Demiris J, Rougeaux S, Hayes GM, Berthouze L, Kuniyoshi Yet al., 1997, Deferred imitation of human head movements by an active stereo vision head, 6th IEEE International Workshop on Robot and Human Communication (RO-MAN 97), Publisher: IEEE, Pages: 88-93

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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