Publications
270 results found
Takacs B, Demiris Y, 2008, Balancing Spectral Clustering for Segmenting Spatio-Temporal Observations of Multi-Agent Systems, 8th IEEE International Conference on Data Mining, Publisher: IEEE COMPUTER SOC, Pages: 580-587, ISSN: 1550-4786
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- Citations: 44
Carlson T, Demiris Y, 2008, Human-wheelchair collaboration through prediction of intention and adaptive assistance, IEEE International Conference on Robotics and Automation, Publisher: IEEE, Pages: 3926-3931, ISSN: 1050-4729
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- Citations: 58
Demiris Y, Khadhouri B, 2008, Content-based control of goal-directed attention during human action perception, Interaction Studies: social behaviour and communication in biological and artificial systems, Vol: 9, Pages: 353-376
Tidemann A, Demiris Y, 2008, A Drum Machine That Learns to Groove, 31st Annual German Conference on Artificial Intelligence, Publisher: SPRINGER-VERLAG BERLIN, Pages: 144-+, ISSN: 0302-9743
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- Citations: 1
Tidemann A, Demiris Y, 2008, Groovy Neural Networks, 18th European Conference on Artificial Intelligence, Publisher: I O S PRESS, Pages: 271-275, ISSN: 0922-6389
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- Citations: 2
, 2008, Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, HRI 2008, Amsterdam, The Netherlands, March 12-15, 2008, Publisher: ACM
Moreno JC, Pons JL, Rocon E, et al., 2008, A hybrid method based on fuzzy inference and non-linear oscillators for real-time control of gait, 1st International Conference on Bio-Inspired Systems and Signal Processing, Publisher: INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION, Pages: 44-51
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- Citations: 2
Takács B, Butler S, Demiris Y, 2007, Multi-agent Behaviour Segmentation via Spectral Clustering, AAAI-2007 Workshop on Plan, Activity and Intention Recognition (PAIR), Publisher: AAAI, Pages: 74-81
We examine the application of spectral clustering for breaking up the behaviour of a multi-agent system in space and time into smaller, independent elements. We extend the clustering into the temporal domain and propose a novel similarity measure, which is shown to possess desirable temporal properties when clustering multi-agent behaviour. We also propose a technique to add knowledge about events of multi-agent interaction with different importance. We apply spectral clustering with this measure for analysing behaviour in a strategic game.
Demiris Y, 2007, Prediction of intent in robotics and multi-agent systems., Cognitive Processing, Vol: 8, Pages: 151-158, ISSN: 1612-4782
Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches.
Demiris Y, Billard A, 2007, Special Issue on Robot Learning by Observation, Demonstration, and Imitation, IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol: 37, Pages: 254-255, ISSN: 1083-4419
This special issue contains selected extended contributions from both the Adaptation in Artificial and Biological Systems symposium held in Hertforshire in 2006 and the wider academic community following a public call for papers in 2006. The papers presented serve as a good illustration of the challenges faced by robotics researchers today in the field of programming by observation, demonstration, and imitation.
Johnson M, Demiris Y, 2007, Visuo-Cognitive Perspective Taking for Action Recognition, AISB'07: Artificial and Ambient Intelligence, Publisher: AISB, Pages: 262-269
Many excellent architectures exist that allow imitation of actions involving observable goals. In this paper, we develop a Simulation Theory-based architecture that uses continuous visual perspective taking to maintain a persistent model of the demonstrator's knowledge of object locations in dynamic environments; this allows an observer robot to attribute potential actions in the presence of goal occlusions, and predict the unfolding of actions through prediction of visual feedback to the demonstrator. The architecture is tested in robotic experiments, and results show that the approach also allows an observer robot to solve Theory-of-Mind tasks from the 'False Belief' paradigm.
Tidemann A, Demiris Y, 2007, Imitating the Groove: Making Drum Machines more Human, AISB'07: Artificial and Ambient Intelligence, Publisher: AISB, Pages: 232-240
Current music production software allows rapid programming of drum patterns, but programmed patterns often lack the groove that a human drummer will provide, both in terms of being rhythmically too rigid and having no variation for longer periods of time. We have implemented an artificial software drummer that learns drum patterns by extracting user specific variations played by a human drummer. The artificial drummer then builds up a library of patterns it can use in different musical contexts. The artificial drummer models the groove and the variations of the human drummer, enhancing the realism of the produced patterns.
Dearden A, Demiris Y, 2007, From exploration to imitation: using learnt internal models to imitate others, AISB'07: Artificial and Ambient Intelligence, Publisher: AISB, Pages: 218-226
We present an architecture that enables asocial and social learning mechanisms to be combined in a unified framework on a robot. The robot learns two kinds of internal models by interacting with the environment with no a priori knowledge of its own motor system: internal object models are learnt about how its motor system and other objects appear in its sensor data; internal control models are learnt by babbling and represent how the robot controls objects. These asocially-learnt models of the robot’s motor system are used to understand the actions of a human demonstrator on objects that they can both interact with. Knowledge acquired through self-exploration is therefore used as a bootstrapping mechanism to understand others and benefit from their knowledge.
Dearden A, Demiris Y, Grau O, 2007, Learning models of camera control for imitation in football matches, AISB'07: Artificial and Ambient Intelligence, Publisher: AISB, Pages: 227-231
In this paper, we present ongoing work towards a system capable of learning from and imitating the movement of a trained cameraman and his director covering a football match. Useful features such as the pitch and the movement of players in the scene are detected using various computer vision techniques. In simulation, a robotic camera trains its own internal model for how it can affect these features. The movement of a real cameraman in an actual football game can be imitated by using this internal model.
Demiris Y K, Johnson M, 2007, Simulation Theory for Understanding Others: A Robotics Perspective, Imitation and Social Learning in Robots, Humans and Animals: Behavioural Social and Communicative Dimensions, Pages: 89-102
Demiris Y K, 2007, Using Robots to study the mechanisms imitation, Neuroconstructivism: Perspectives and Prospects, Editors: Mareschal, Sirois, Westermann, Publisher: Oxford University Press, Pages: 159-178
Chinellato E, Demiris Y, Del Pobil AP, 2006, Studying the human visual cortex for improving prehension capabilities in robotics, Pages: 184-189
Although other primates have grasping skills, human beings evolved theirs to the extent that a large fraction of our brain is involved in grasping actions. Recent neuroscience findings allow us to depict the outline of a model of visionbased grasp planning that differentiates from the previous ones in that it is the first to rest mainly, if not exclusively, on human physiology. The main theory on which our proposal is based is that of the two streams of the human visual cortex [1]. Although they are evolved for different purposes, being the ventral stream dedicated to perceptual vision, and the dorsal stream to action-oriented vision, they need to collaborate in order to allow proper interaction of human beings with the world. Our framework has been conceived to be applied on a robotic setup, and the design of the different brain areas has been performed taking into account not only biological plausibility, but also practical issues related to engineering constraints.
Dearden A, Demiris Y, Grau O, 2006, Tracking football player movement from a single moving camera using particle filters, European Conference on Visual Media Production (CVMP), Publisher: IET, Pages: 29-37
This paper deals with the problem of tracking football players in a football match using data from a single moving camera. Tracking footballers from a single video source is difficult: not only do the football players occlude each other, but they frequently enter and leave the camera's field of view, making initialisation and destruction of a player's tracking a difficult task. The system presented here uses particle filters to track players. The multiple state estimates used by a particle filter provide an elegant method for maintaining tracking of players following an occlusion. Automated tracking can be achieved by creating and stopping particle filters depending on the input player data.
Dearden A, Demiris Y, Grau O, 2006, Tracking football player movement from a single moving camera using particle filters, Pages: 29-37
Demiris Y, Khadhouri B, 2006, Content-Based Control of Goal-Directed Attention During Human Action Perception, Pages: 226-231
Demiris Y, Khadhouri B, 2006, Hierarchical attentive multiple models for execution and recognition of actions, Robotics and Autonomous Systems, Vol: 54, Pages: 361-369, ISSN: 0921-8890
According to the motor theories of perception, the motor systems of an observer are actively involved in the perception of actions when these are performed by a demonstrator. In this paper we review our computational architecture, HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition), where the motor control systems of a robot are organised in a hierarchical, distributed manner, and can be used in the dual role of (a) competitively selecting and executing an action, and (b) perceiving it when performed by a demonstrator. We subsequently demonstrate that such an arrangement can provide a principled method for the top-down control of attention during action perception, resulting in significant performance gains. We assess these performance gains under a variety of resource allocation strategies.
Simmons G, Demiris Y, 2006, Object Grasping using the Minimum Variance Model, Biological Cybernetics, Vol: 94, Pages: 393-407, ISSN: 0340-1200
Reaching-to-grasp has generally been classified as the coordination of two separate visuomotor processes: transporting the hand to the target object and performing the grip. An alternative view has recently been formed that grasping can be explained as pointing movements performed by the digits of the hand to target positions on the object. We have previously implemented the minimum variance model of human movement as an optimal control scheme suitable for control of a robot arm reaching to a target. Here, we extend that scheme to perform grasping movements with a hand and arm model. Since the minimum variance model requires that signal-dependent noise be present on the motor commands to the actuators of the movement, our approach is to plan the reach and the grasp separately, in line with the classical view, but using the same computational model for pointing, in line with the alternative view. We show that our model successfully captures some of the key characteristics of human grasping movements, including the observations that maximum grip size increases with object size (with a slope of approximately 0.8) and that this maximum grip occurs at 60-80% of the movement time. We then use our model to analyse contributions to the digit end-point variance from the two components of the grasp (the transport and the grip). We also briefly discuss further areas of investigation that are prompted by our model.
Veskos P, Demiris Y, 2006, Neuro-mechanical entrainment in a bipedal robotic walking platform, AISB'06: Adaptation in Artificial and Biological Systems, Publisher: AISB, Pages: 78-84
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace.
Demiris Y, Simmons G, 2006, Perceiving the unusual: temporal properties of hierarchical motor representations for action perception, Neural Networks, Vol: 19, Pages: 272-284, ISSN: 0893-6080
Recent computational approaches to action imitation have advocated the use of hierarchical representations in the perception and imitation of demonstrated actions. Hierarchical representations present several advantages, with the main one being their ability to process information at multiple levels of detail. However, the nature of the hierarchies in these approaches has remained relatively unsophisticated, and their relation with biological evidence has not been investigated in detail, in particular with respect to the timing of movements. Following recent neuroscience work on the modulation of the premotor mirror neuron activity during the observation of unpredictable grasping movements, we present here an implementation of our HAMMER architecture using the minimum variance model for implementing reaching and grasping movements that have biologically plausible trajectories. Subsequently, we evaluate the performance of our model in matching the temporal dynamics of the modulation of cortical excitability during the passive observation of normal and unpredictable movements of human demonstrators.
Dearden A, Demiris Y, 2006, Active learning of probabilistic forward models in visuo-motor development, AISB'06: Adaptation in Artificial and Biological Systems, Publisher: AISB, Pages: 176-183
Forward models enable both robots and humans to predict the sensory consequences of their motor actions. To learn its own forward models a robot needs to experiment with its own motor system, in the same way that human infants need to babble as a part of their motor development. In this paper we investigate how this babbling with the motor system can be influenced by the forward models’ own knowledge of their predictive ability. By spending more time babbling in regions of motor space that require more accuracy in the forward model, the learning time can be reduced. The key to guiding this exploration is the use of probabilistic forward models, which are capable of learning and predicting not just the sensory consequence of a motor command, but also an estimate of how accurate this prediction is. An experiment was carried out to test this theory on a robotic pan tilt camera.
Veskos P, Demiris Y, 2006, Experimental comparison of the van der Pol and Rayleigh nonlinear oscillators for a robotic swinging task, AISB'06: Adaptation in Artificial and Biological Systems, Publisher: AISB, Pages: 197-202
In this paper, the effects of different lower-level building blocks of a robotic swinging system are explored, from the perspective of motor skill acquisition. The van der Pol and Rayleigh oscillators are used to entrain to the system’s natural dynamics, with two different network topologies being used: a symmetric and a hierarchical one. Rayleigh outperformed van der Pol regarding maximum oscillation amplitudes for every morphological configuration examined. However, van der Pol started large amplitude relaxation oscillations faster, attaining better performance during the first half of the transient period. Hence, even though there are great similarities between the oscillators, differences in their resultant behaviours are more pronounced than originally expected.
Chinellato E, Demiris Y, del Pobil AP, 2006, Studying the human visual cortex for achieving action-perception coordination with robots, Artificial Intelligence and Soft Computing, Editors: del Pobil, Publisher: Acta Press, Anaheim, CF, USA, Pages: 184-189
Johnson M, Demiris Y, 2005, Perceptual perspective taking and action recognition, Pages: 301-308, ISSN: 1729-8806
Robots that operate in social environments need to be able to recognise and understand the actions of other robots, and humans, in order to facilitate learning through imitation and collaboration. The success of the simulation theory approach to action recognition and imitation relies on the ability to take the perspective of other people, so as to generate simulated actions from their point of view. In this paper, simulation of visual perception is used to recreate the visual egocentric sensory space and egocentric behaviour space of an observed agent, and through this increase the accuracy of action recognition. To demonstrate the approach, experiments are performed with a robot attributing perceptions to and recognising the actions of a second robot.
Johnson M, Demiris Y, 2005, Perceptual Perspective Taking and Action Recognition, International Journal of Advanced Robotic Systems, Vol: 2, Pages: 301-308, ISSN: 1729-8806
Robots that operate in social environments need to be able to recognise and understand the actions of other robots, and humans, in order to facilitate learning through imitation and collaboration. The success of the simulation theory approach to action recognition and imitation relies on the ability to take the perspective of other people, so as to generate simulated actions from their point of view. In this paper, simulation of visual perception is used to recreate the visual egocentric sensory space and egocentric behaviour space of an observed agent, and through this increase the accuracy of action recognition. To demonstrate the approach, experiments are performed with a robot attributing perceptions to and recognising the actions of a second robot.
Simmons G, Demiris Y, 2005, Optimal robot arm control using the minimum variance model, Journal of Robotic Systems, Vol: 22, Pages: 677-690, ISSN: 0741-2223
Models of human movement from computational neuroscience provide a starting point for building a system that can produce flexible adaptive movement on a robot. There have been many computational models of human upper limb movement put forward, each attempting to explain one or more of the stereotypical features that characterize such movements. While these models successfully capture some of the features of human movement, they often lack a compelling biological basis for the criteria they choose to optimize. One that does provide such a basis is the minimum variance model (and its extension—task optimization in the presence of signal-dependent noise). Here, the variance of the hand position at the end of a movement is minimized, given that the control signals on the arm's actuators are subject to random noise with zero mean and variance proportional to the amplitude of the signal. Since large control signals, required to move fast, would have higher amplitude noise, the speed-accuracy trade-off emerges as a direct result of the optimization process. We chose to implement a version of this model that would be suitable for the control of a robot arm, using an optimal control scheme based on the discrete-time linear quadratic regulator. This implementation allowed us to examine the applicability of the minimum variance model to producing humanlike movement. In this paper, we describe our implementation of the minimum variance model, both for point-to-point reaching movements and for more complex trajectories involving via points. We also evaluate its performance in producing humanlike movement and show its advantages over other optimization based models (the well-known minimum jerk and minimum torque-change models) for the control of a robot arm.
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