191 results found
Martins MF, Demiris Y, 2010, Impact of Human Communication in a Multi-teacher, Multi-robot Learning by Demonstration System., AAMAS'10 Workshop on Agents Learning Interactively from Human Teachers
A wide range of architectures have been proposed within the areas of learning by demonstration and multi-robot coordination. These areas share a common issue: how humans and robots share information and knowledge among themselves. This paper analyses the impact of communication between human teachers during simultaneous demonstration of task execution in the novel Multi-robot Learning by Demonstration domain, using the MRLbD architecture. The performance is analysed in terms of time to task completion, as well as the quality of the multi-robot joint action plans. Participants with different levels of skills taught real robots solutions for a furniture moving task through teleoperation. The experimental results provided evidence that explicit communication between teachers does not necessarily reduce the time to complete a task, but contributes to the synchronisation of manoeuvres, thus enhancing the quality of the joint action plans generated by the MRLbD architecture.
Butler S, Demiris Y, 2010, Using a Cognitive Architecture for Opponent Target Prediction, AISB'10: International Symposium on AI & Games, Publisher: AISB, Pages: 55-62
One of the most important aspects of a compelling game AI is that it anticipates the player’s actions and responds to them in a convincing manner. The first step towards doing this is to understand what the player is doing and predict their possible future actions. In this paper we show an approach where the AI system focusses on testing hypotheses made about the player’s actions using an implementation of a cognitive architecture inspired by the simulation theory of mind. The application used in this paper is to predict the target that the player is heading towards, in an RTS-style game. We improve the prediction accuracy and reduce the number of hypotheses needed by using path planning and path clustering.
Carlson T, Demiris Y, 2010, Increasing Robotic Wheelchair Safety With Collaborative Control: Evidence from Secondary Task Experiments, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 5582-5587, ISSN: 1050-4729
Pitt J, Demiris Y, Polak J, 2010, Converging Bio-inspired Robotics and Socio-inspired Agents for Intelligent Transportation Systems, 9th International Conference on Artificial Immune Systems (ICARIS 2010), Publisher: SPRINGER-VERLAG BERLIN, Pages: 304-+, ISSN: 0302-9743
Butler S, Demiris Y, 2010, Partial observability during predictions of the opponent's movements in an RTS game, Symposium on Computational Intelligence and Games (CIG), Publisher: IEEE, Pages: 46-53
In RTS-style games it is important to be able to predict the movements of the opponent's forces to have the best chance of performing appropriate counter-moves. Resorting to using perfect global state information is generally considered to be `cheating' by the player, so to perform such predictions scouts (or observers) must be used to gather information. This means being in the right place at the right time to observe the opponent. In this paper we show the effect of imposing partial observability onto an RTS game with regard to making predictions, and we compare two different mechanisms that decide where best to direct the attention of the observers to maximise the benefit of predictions.
Wu Y, Demiris Y, 2009, Efficient Template-based Path Imitation by Invariant Feature Mapping, International Conference on Robotics and Biomimetics (ROBIO), Publisher: IEEE, Pages: 913-918
We propose a novel approach for robot movement imitation that is suitable for robotic arm movement in tasks such as reaching and grasping. This algorithm selects a previously observed path demonstrated by an agent and generates a path in a novel situation based on pairwise mapping of invariant feature locations present in both the demonstrated and the new scenes using minimum distortion and minimum energy strategies. This One-Shot Learning algorithm is capable of not only mapping simple point-to-point paths but also adapting to more complex tasks such as involvement of forced waypoints. As compared to traditional methodologies, our work does not require extensive training for generalisation as well as expensive run-time computation for accuracy. Cross-validation statistics of grasping experiments show great similarity between the paths produced by human subjects and the proposed algorithm.
Demiris Y, 2009, Knowing when to assist: developmental issues in lifelong assistive robotics., Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, Publisher: IEEE, Pages: 3357-3360, ISSN: 1557-170X
Children and adults with sensorimotor disabilities can significantly increase their autonomy through the use of assistive robots. As the field progresses from short-term, task-specific solutions to long-term, adaptive ones, new challenges are emerging. In this paper a lifelong methodological approach is presented, that attempts to balance the immediate context-specific needs of the user, with the long-term effects that the robot's assistance can potentially have on the user's developmental trajectory.
Takacs B, Demiris Y, 2009, Multi-robot plan adaptation by constrained minimal distortion feature mapping, 2009 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE
Butler S, Demiris Y, 2009, Predicting the Movements of Robot Teams Using Generative Models, International Symposium on Distributed Autonomous Robotic Systems (DARS), Publisher: Springer, Pages: 533-542
When a robot plans its actions within an environment containing multiple robots, it is often necessary to take into account the actions and movements of the other robots to either avoid, counter, or cooperate with them, depending on the scenario. Our predictive system is based on the biologically-inspired, simulation theoretic approach that uses internal generative models in single-robot applications. Here, we move beyond the single-robot case to illustrate how these generative models can predict the movements of the opponent’s robots, when applied to an adversarial scenario involving two robot teams. The system is able to recognise whether the robots are attacking or defending, and the formation they are moving in. It can then predict their future movements based on the recognised model. The results confirm that the speed of recognition and the accuracy of prediction depend on how well the models match the robots’ observed behaviour.
Carlson T, Demiris Y, 2009, Using Visual Attention to Evaluate Collaborative Control Architectures for Human Robot Interaction, AISB'09: New Frontiers in Human-Robot Interaction
Collaborative control architectures assist human users in performing tasks, without undermining their capabilities or curtailing the natural development of their skills. In this study, we evaluate our collaborative control architecture by investigating the visual attention patterns of robotic wheelchair users. Our initial hypothesis stated that the user would require less visual attention for driving, whilst they are being assisted by the collaborative system, thus allowing them to concentrate on higher level cognitive tasks, such as planning. However, our analysis of eye gaze patterns—as recorded by ahead mounted eye tracking system—supports the opposite conclusion: that patterns of saccadic activation increase and become more chaotic under the assisted mode. Our findings highlight the necessity for techniques that assist the user in forming an appropriate mental model of the collaborative control architecture.
Demiris Y, Carlson T, 2009, Lifelong robot-assisted mobility: models, tools, and challenges, IET Conference on Assisted Living 2009, Publisher: IET
Increasing the autonomy of users with disabilities through robot-assisted mobility has the potential of facilitating their sensorimotor and social development, as well as reducing the burden of caring for such populations in both inpatient and outpatient settings. While techniques for task-specific assistance exist, they are largely focused on satisfying short- term goals, utilising stationary user models. For lifelong users and particularly for those with rapidly changing sensorimotor skills (for example very young children), adaptive models that take into consideration these developmental trajectories are becoming very important. In this paper, we present our approach to lifelong user models for robot-assisted mobility, and discuss existing models and tools, as well as challenges that remain ahead.
Tidemann A, Ozturk P, Demiris Y, 2009, A Groovy Virtual Drumming Agent, 9th International Conference on Intelligent Virtual Agents, Publisher: SPRINGER-VERLAG BERLIN, Pages: 104-+, ISSN: 0302-9743
Demiris Y, Meltzoff A, 2008, The Robot in the Crib: A Developmental Analysis of Imitation Skills in Infants and Robots., Infant and Child Development, Vol: 17, Pages: 43-53, ISSN: 1522-7227
Interesting systems, whether biological or artificial, develop. Starting from some initial conditions, they respond to environmental changes, and continuously improve their capabilities. Developmental psychologists have dedicated significant effort to studying the developmental progression of infant imitation skills, because imitation underlies the infant's ability to understand and learn from his or her social environment. In a converging intellectual endeavour, roboticists have been equipping robots with the ability to observe and imitate human actions because such abilities can lead to rapid teaching of robots to perform tasks. We provide here a comparative analysis between studies of infants imitating and learning from human demonstrators, and computational experiments aimed at equipping a robot with such abilities. We will compare the research across the following two dimensions: (a) initial conditions-what is innate in infants, and what functionality is initially given to robots, and (b) developmental mechanisms-how does the performance of infants improve over time, and what mechanisms are given to robots to achieve equivalent behaviour. Both developmental science and robotics are critically concerned with: (a) how their systems can and do go 'beyond the stimulus' given during the demonstration, and (b) how the internal models used in this process are acquired during the lifetime of the system.
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
Demiris Y, Khadhouri B, 2008, Content-based control of goal-directed attention during human action perception, Interaction Studies, Vol: 9, Pages: 353-376, ISSN: 1572-0373
During the perception of human actions by robotic assistants, the robotic assistant needs to direct its computational and sensor resources to relevant parts of the human action. In previous work we have introduced HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition) (Demiris and Khadhouri, 2006), a computational architecture that forms multiple hypotheses with respect to what the demonstrated task is, and multiple predictions with respect to the forthcoming states of the human action. To confirm their predictions, the hypotheses request information from an attentional mechanism, which allocates the robot's resources as a function of the saliency of the hypotheses. In this paper we augment the attention mechanism with a component that considers the content of the hypotheses' requests, with respect to the content's reliability, utility and cost. This content-based attention component further optimises the utilisation of the resources while remaining robust to noise. Such computational mechanisms are important for the development of robotic devices that will rapidly respond to human actions, either for imitation or collaboration purposes.
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
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
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
Sastoque JCM, Rovira JLP, Lima ERD, et al., 2008, A Hybrid Method based on Fuzzy Inference and Non-Linear Oscillators for Real-Time Control of Gait., Publisher: INSTICC - Institute for Systems and Technologies of Information, Control and Communication, Pages: 44-51
, 2008, Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, HRI 2008, Amsterdam, The Netherlands, March 12-15, 2008, Publisher: ACM
Fong T, Dautenhahn K, Scheutz M, et al., 2008, The Third International Conference on Human-Robot Interaction., AI Magazine, Vol: 29, Pages: 77-78
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
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.
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.
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.
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.
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.
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
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