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  • Conference paper
    Korkinof D, Demiris Y, 2013,

    Online Quantum Mixture Regression for Trajectory Learning by Demonstration

    , IROS 2013, Publisher: IEEE, Pages: 3222-3229

    In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quan- tum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community.
  • Conference paper
    Soh H, Demiris Y, 2013,

    When and how to help: An iterative probabilistic model for learning assistance by demonstration

    , International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3230-3236, ISSN: 2153-0858

    Crafting a proper assistance policy is a difficult endeavour but essential for the development of robotic assistants. Indeed, assistance is a complex issue that depends not only on the task-at-hand, but also on the state of the user, environment and competing objectives. As a way forward, this paper proposes learning the task of assistance through observation; an approach we term Learning Assistance by Demonstration (LAD). Our methodology is a subclass of Learning-by-Demonstration (LbD), yet directly addresses difficult issues associated with proper assistance such as when and how to appropriately assist. To learn assistive policies, we develop a probabilistic model that explicitly captures these elements and provide efficient, online, training methods. Experimental results on smart mobility assistance — using both simulation and a real-world smart wheelchair platform — demonstrate the effectiveness of our approach; the LAD model quickly learns when to assist (achieving an AUC score of 0.95 after only one demonstration) and improves with additional examples. Results show that this translates into better task-performance; our LAD-enabled smart wheelchair improved participant driving performance (measured in lap seconds) by 20.6s (a speedup of 137%), after a single teacher demonstration.
  • Conference paper
    Korkinof D, Demiris Y, 2013,

    Online Quantum Mixture Regression for Trajectory Learning by Demonstration

    , International Conference on Intelligent Systems and Robots (IROS), Publisher: IEEE, Pages: 3222-3229, ISSN: 2153-0858

    In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community.
  • Conference paper
    Ros R, Demiris Y, 2013,

    Creative Dance: An Approach for Social Interaction between Robots and Children

    , 4th International Workshop on Human Behavior Understanding (HBU), Publisher: Springer, Pages: 40-51, ISSN: 0302-9743

    In this paper we discuss the potential of using a dance robot tutor with children in the context of creative dance to study child-robot interaction through several encounters. We have taken part of dance sessions in order to extract strategies and models to inspire and justify the design of a robot dance tutor. Moreover, we present implementation details and preliminary results on a pilot study to extract initial feedback to further improve and test our system with a broader children population.
  • Journal article
    Ognibene D, Chinellato E, Sarabia M, Demiris Yet al., 2013,

    Contextual action recognition and target localization with an active allocation of attention on a humanoid robot

    , Bioinspiration & Biomimetics, Vol: 8

    Exploratory gaze movements are fundamental for gathering the most relevant information regarding the partner during social interactions. Inspired by the cognitive mechanisms underlying human social behaviour, we have designed and implemented a system for a dynamic attention allocation which is able to actively control gaze movements during a visual action recognition task exploiting its own action execution predictions. Our humanoid robot is able, during the observation of a partner's reaching movement, to contextually estimate the goal position of the partner's hand and the location in space of the candidate targets. This is done while actively gazing around the environment, with the purpose of optimizing the gathering of information relevant for the task. Experimental results on a simulated environment show that active gaze control, based on the internal simulation of actions, provides a relevant advantage with respect to other action perception approaches, both in terms of estimation precision and of time required to recognize an action. Moreover, our model reproduces and extends some experimental results on human attention during an action perception.
  • Conference paper
    Ognibene D, Demiris Y, 2013,

    Towards Active Event Recognition

    , International Joint Conference on Artificial Intelligence (IJCAI), Publisher: AIII Press, Pages: 2495-2501

    Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision.We propose a probabilistic generative framework based on a mixture of Kalman filters and information gain maximisation that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration.Interestingly, the sensors-control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness.Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems.
  • Journal article
    Chatzis S, Demiris Y, 2013,

    The Infinite-Order Conditional Random Field Model for Sequential Data Modeling

    , IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 6, Pages: 1523-1534, ISSN: 0162-8828

    Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations can capture only one- or few-timestep interactions and neglect higher order dependences, which are potentially useful in many real-life sequential data modeling applications. To resolve these issues, in this paper we introduce a novel CRF formulation, based on the postulation of an energy function which entails infinitely long time-dependences between the modeled data. Building blocks of our novel approach are: 1) the sequence memoizer (SM), a recently proposed nonparametric Bayesian approach for modeling label sequences with infinitely long time dependences, and 2) a mean-field-like approximation of the model marginal likelihood, which allows for the derivation of computationally efficient inference algorithms for our model. The efficacy of the so-obtained infinite-order CRF model is experimentally demonstrated.
  • Journal article
    Petit M, Lallée S, Boucher J-D, Pointeau G, Cheminade P, Ognibene D, Chinellato E, Pattacini U, Gori I, Martinez-Hernandez U, Barron-Gonzalez H, Inderbitzin M, Luvizotto A, Vouloutsi V, Demiris Y, Metta G, Dominey PFet al., 2013,

    The Coordinating Role of Language in Real-Time Multi-Modal Learning of Cooperative Tasks

    , IEEE Transactions on Autonomous Mental Development, Vol: 5, Pages: 3-17, ISSN: 1943-0604

    One of the defining characteristics of human cognition is our outstanding capacity to cooperate. A central requirement for cooperation is the ability to establish a “shared plan”-which defines the interlaced actions of the two cooperating agents-in real time, and even to negotiate this shared plan during its execution. In the current research we identify the requirements for cooperation, extending our earlier work in this area. These requirements include the ability to negotiate a shared plan using spoken language, to learn new component actions within that plan, based on visual observation and kinesthetic demonstration, and finally to coordinate all of these functions in real time. We present a cognitive system that implements these requirements, and demonstrate the system's ability to allow a Nao humanoid robot to learn a nontrivial cooperative task in real-time. We further provide a concrete demonstration of how the real-time learning capability can be easily deployed on a different platform, in this case the iCub humanoid. The results are considered in the context of how the development of language in the human infant provides a powerful lever in the development of cooperative plans from lower-level sensorimotor capabilities.
  • Journal article
    Belpaeme T, Baxter PE, Read R, Wood R, Cuayáhuitl H, Kiefer B, Racioppa S, Kruijff-Korbayová I, Athanasopoulos G, Enescu V, Looije R, Neerincx M, Demiris Y, Ros-Espinoza R, Beck A, Cañamero L, Hiolle A, Lewis M, Baroni I, Nalin M, Cosi P, Paci G, Tesser F, Sommavilla G, Humbert Ret al., 2013,

    Multimodal Child-Robot Interaction: Building Social Bonds

    , Journal of Human-Robot Interaction, Vol: 1, Pages: 33-53

    For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competen- cies and integrating them to form an autonomous robotic system for evaluation “in the wild.” The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions.
  • Conference paper
    Sarabia M, Le Mau T, Soh H, Naruse S, Poon C, Liao Z, Tan KC, Lai ZJ, Demiris Yet al., 2013,

    iCharibot : Design and Field Trials of a Fundraising Robot

    , International Conference on Social Robotics (ICSR 2013), Publisher: Springer, Pages: 412-421

    In this work, we address the problem of increasing charitable donations through a novel, engaging fundraising robot: the Imperial Charity Robot (iCharibot). To better understand how to engage passers-by, we conducted a field trial in outdoor locations at a busy area in London, spread across 9 sessions of 40 minutes each. During our experiments, iCharibot attracted 679 people and engaged with 386 individuals. Our results show that interactivity led to longer user engagement with the robot. Our data further suggests both saliency and interactivity led to an increase in the total donation amount. These findings should prove useful for future design of robotic fundraisers in particular and for social robots in general.
  • Conference paper
    Sarabia M, Demiris Y, 2013,

    A Humanoid Robot Companion for Wheelchair Users

    , International Conference on Social Robotics (ICSR), Publisher: Springer, Pages: 432-441

    In this paper we integrate a humanoid robot with a powered wheelchair with the aim of lowering the cognitive requirements needed for powered mobility. We propose two roles for this companion: pointing out obstacles and giving directions. We show that children enjoyed driving with the humanoid companion by their side during a field-trial in an uncontrolled environment. Moreover, we present the results of a driving experiment for adults where the companion acted as a driving aid and conclude that participants preferred the humanoid companion to a simulated companion. Our results suggest that people will welcome a humanoid companion for their wheelchairs.
  • Conference paper
    Su Y, Wu Y, Lee K, Du Z, Demiris Yet al., 2012,

    Robust Grasping for an Under-actuated Anthropomorphic Handunder Object Position Uncertainty

    , Osaka, Japan, International Conference on Humanoid Robots (Humanoids), Publisher: IEEE, Pages: 719-725

    This paper presents a grasp execution strategy for grasping an object with one trial when there is uncertainty in the object position. This strategy is based on three grasping components: 1) robust grasp trajectory planning which can cope with reasonable amount of initial object position error, 2) sensor-based grasp adaptation, and 3) compliant characteristics of the under actuated mechanism. This strategy is implemented and tested on the iCub humanoid robot. Two experiments and a demo of the iCub robot playing the Towers of Hanoi game are carried out to verify our system. The results demonstrate that the iCub using this approach can successfully grasp objects under certain position error with its under-actuated anthropomorphic hand.
  • Journal article
    Chatzis SP, Demiris Y, 2012,

    Nonparametric mixtures of Gaussian processes with power-law behavior

    , IEEE Transactions on Neural Networks, Vol: 23, Pages: 1862-1871, ISSN: 2162-237X

    Gaussian processes (GPs) constitute one of the most important Bayesian machine learning approaches, based on a particularly effective method for placing a prior distribution overthe space of regression functions. Several researchers have considered postulating mixtures of Gaussian processes as a means ofdealing with non-stationary covariance functions, discontinuities, multi-modality, and overlapping output signals. In existing works, mixtures of Gaussian processes are based on the introduction of a gating function defined over the space of model input variables. This way, each postulated mixture component Gaussian process is effectively restricted in a limited subset of the input space. In this work, we follow a different approach: We consider a fully generative nonparametric Bayesian model with power-law behavior, generating Gaussian processes over the whole input space of the learned task. We provide an efficient algorithm for model inference, based on the variational Bayesian framework, and exhibit its efficacy using benchmark and real-world datasets.
  • Conference paper
    Lee K, Kim TK, Demiris Y, 2012,

    Learning Action Symbols for Hierarchical Grammar Induction

    , Tsukuba, Japan, International Conference on Pattern Recognition (ICPR), Publisher: IEEE, Pages: 3778-3782

    We present an unsupervised method of learning action symbols from video data, which self-tunes the number of symbols to effectively build hierarchical activity grammars. A video stream is given as a sequence of unlabeled segments. Similar segments are incrementally grouped to form a hierarchical tree structure. The tree is cut into clusters where each cluster is used to train an action symbol. Our goal is to find a good set of clusters i.e. symbols where regularities are best captured in the learned representation, i.e. induced grammar. Our method has two-folds: 1) Create a candidate set of symbols from initial clusters, 2) Build an activity grammar and measure model complexity and likelihood to assess the quality of the candidate set of symbols. We propose a balanced model comparison method which avoids the problem commonly found in model complexity computations where one measurement term dominates the other. Our experiments on the towers of Hanoi and human dancing videos show that our method can discover the optimal number of action symbols effectively.
  • Journal article
    Chatzis SP, Demiris Y, 2012,

    A Reservoir-Driven Non-Stationary Hidden Markov Model

    , Pattern Recognition, Vol: 45, Pages: 3985-3996

    In this work, we propose a novel approach towards sequential data modeling that leverages the strengths of hidden Markov models and echo-state networks (ESNs) in the context of non-parametric Bayesian inference approaches. We introduce a non-stationary hidden Markov model, the time-dependent state transition probabilities of which are driven by a high-dimensional signal that encodes the whole history of the modeled observations, namely the state vector of a postulated observations-driven ESN reservoir. We derive an efficient inference algorithm for our model under the variational Bayesian paradigm, and we examine the efficacy of our approach considering a number of sequential data modeling applications.
  • Conference paper
    Soh H, Demiris Y, 2012,

    Towards Early Mobility Independence: An Intelligent Paediatric Wheelchair with Case Studies

    , IROS Workshop on Progress, Challenges and Future Perspectives in Navigation and Manipulation Assistance for Robotic Wheelchairs

    Standard powered wheelchairs are still heavily dependent on the cognitive capabilities of users. Unfortunately, this excludes disabled users who lack the required problem-solving and spatial skills, particularly young children. For these children to be denied powered mobility is a crucial set-back; exploration is important for their cognitive, emotional and psychosocial development. In this paper, we present a safer paediatric wheelchair: the Assistive Robot Transport for Youngsters (ARTY). The fundamental goal of this research is to provide a key-enabling technology to young children who would otherwise be unable to navigate independently in their environment. In addition to the technical details of our smart wheelchair, we present user-trials with able-bodied individuals as well as one 5-year-old child with special needs. ARTY promises to provide young children with "early access" to the path towards mobility independence.
  • Conference paper
    Soh H, Su Y, Demiris Y, 2012,

    Online Spatio-Temporal Gaussian Process Experts with Application to Tactile Classification

    , International Conference on Intelligent Robots and Systems, IROS, Publisher: IEEE, Pages: 4489-4496, ISSN: 2153-0858

    In this work, we are primarily concerned with robotic systems that learn online and continuously from multi-variate data-streams. Our first contribution is a new recursive kernel, which we have integrated into a sparse Gaussian Process to yield the Spatio-Temporal Online Recursive Kernel Gaussian Process (STORK-GP). This algorithm iteratively learns from time-series, providing both predictions and uncertainty estimates. Experiments on benchmarks demonstrate that our method achieves high accuracies relative to state-of-the-art methods. Second, we contribute an online tactile classifier which uses an array of STORK-GP experts. In contrast to existing work, our classifier is capable of learning new objects as they are presented, improving itself over time. We show that our approach yields results comparable to highly-optimised offline classification methods. Moreover, we conducted experiments with human subjects in a similar online setting with true-label feedback and present the insights gained.
  • Journal article
    Chatzis SP, Demiris Y, 2012,

    The echo state conditional random field model for sequential data modeling

    , Expert Systems With Applications, Vol: 39, Pages: 10303-10309, ISSN: 0957-4174

    Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic such examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning, as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations do not account for temporal dependencies between the observed variables – they only postulate Markovian interdependencies between the predicted label variables. To resolve these issues, in this paper we propose a non-linear hierarchical CRF formulation that combines the power of echo state networks to extract high level temporal features with the graphical framework of CRF models, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks.
  • Conference paper
    Chatzis SP, Korkinof D, Demiris Y, 2012,

    A Spatially-Constrained Normalized Gamma Process for Data Clustering

    , International Conference on Artificial Intelligence Applications and Innovations, AIA 2012, Publisher: Springer, Pages: 337-346

    In this work, we propose a novel nonparametric Bayesian method for clustering of data with spatial interdependencies. Specifically, we devise a novel normalized Gamma process, regulated by a simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. As a result of its construction, the proposed model allows for introducing spatial dependencies in the clustering mechanics of the normalized Gamma process, thus yielding a novel nonparametric Bayesian method for spatial data clustering. We derive an efficient truncated variational Bayesian algorithm for model inference. We examine the efficacy of our approach by considering an image segmentation application using a real-world dataset. We show that our approach outperforms related methods from the field of Bayesian nonparametrics, including the infinite hidden Markov random field model, and the Dirichlet process prior.
  • Conference paper
    Ognibene D, Chinellato E, Sarabia M, Demiris Yet al., 2012,

    Towards Contextual Action Recognition and Target Localization with Active Allocation of Attention

    , Living Machines, Publisher: Springer, Pages: 192-203, ISSN: 0302-9743

    Exploratory gaze movements are fundamental for gathering the most relevant information regarding the partner during social interactions. We have designed and implemented a system for dynamic attention allocation which is able to actively control gaze movements during a visual action recognition task. During the observation of a partner’s reaching movement, the robot is able to contextually estimate the goal position of the partner hand and the location in space of the candidate targets, while moving its gaze around with the purpose of optimizing the gathering of information relevant for the task. Experimental results on a simulated environment show that active gaze control provides a relevant advantage with respect to typical passive observation, both in term of estimation precision and of time required for action recognition.
  • Journal article
    Chatzis SP, Demiris Y, 2012,

    A Sparse Nonparametric Hierarchical Bayesian Approach Towards Inductive Transfer for Preference Modeling

    , Expert Systems with Applications, Vol: 39, Pages: 7235-7246

    In this paper, we present a novel methodology for preference learning based on the concept of inductive transfer. Specifically, we introduce a nonparametric hierarchical Bayesian multitask learning approach, based on the notion that human subjects may cluster together forming groups of individuals with similar preference rationale (but not identical preferences). Our approach is facilitated by the utilization of a Dirichlet process prior, which allows for the automatic inference of the most appropriate number of subject groups (clusters), as well as the employment of the automatic relevance determination (ARD) mechanism, giving rise to a sparse nature for our model, which significantly enhances its computational efficiency. We explore the efficacy of our novel approach by applying it to both a synthetic experiment and a real-world music recommendation application. As we show, our approach offers a significant enhancement in the effectiveness of knowledge transfer in statistical preference learning applications, being capable of correctly inferring the actual number of human subject groups in a modeled dataset, and limiting knowledge transfer only to subjects belonging to the same group (wherein knowledge transferability is more likely).
  • Journal article
    Chatzis SP, Korkinof D, Demiris Y, 2012,

    A nonparametric Bayesian approach toward robot learning by demonstration

    , Robotics and Autonomous Systems, Vol: 60, Pages: 789-802, ISSN: 0921-8890

    In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose. A major limitation of GMR models concerns automatic selection of the proper number of model states, i.e., the number of model component densities. Existing methods, including likelihood- or entropy-based criteria, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally investigate its efficacy as a robot learning by demonstration methodology, considering a number of demanding robot learning by demonstration scenarios.
  • Journal article
    Carlson T, Demiris Y, 2012,

    Collaborative Control of a Robotic Wheelchair: Evaluation of Performance, Attention and Workload

    , IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol: 42, Pages: 876-888, ISSN: 1083-4419

    Powered wheelchair users often struggle to drive safely and effectively and, in more critical cases, can only get around when accompanied by an assistant. To address these issues, we propose a collaborative control mechanism that assists users as and when they require help. The system uses a multiple-hypothesis method to predict the driver's intentions and, if necessary, adjusts the control signals to achieve the desired goal safely. The main emphasis of this paper is on a comprehensive evaluation, where we not only look at the system performance but also, perhaps more importantly, characterize the user performance in an experiment that combines eye tracking with a secondary task. Without assistance, participants experienced multiple collisions while driving around the predefined route. Conversely, when they were assisted by the collaborative controller, not only did they drive more safely but also they were able to pay less attention to their driving, resulting in a reduced cognitive workload. We discuss the importance of these results and their implications for other applications of shared control, such as brain-machine interfaces, where it could be used to compensate for both the low frequency and the low resolution of the user input.
  • Conference paper
    Lee K, Kim TK, Demiris Y, 2012,

    Learning Reusable Task Components using Hierarchical Activity Grammars with Uncertainties

    , St. Paul, Minnesota, USA, Publisher: IEEE, Pages: 1994-1999

    We present a novel learning method using activity grammars capable of learning reusable task components from a reasonably small number of samples under noisy conditions. Our linguistic approach aims to extract the hierarchical structure of activities which can be recursively applied to help recognize unforeseen, more complicated tasks that share the same underlying structures. To achieve this goal, our method 1) actively searches for frequently occurring action symbols that are subset of input samples to effectively discover the hierarchy, and 2) explicitly takes into account the uncertainty values associated with input symbols due to the noise inherent in low-level detectors. In addition to experimenting with a synthetic dataset to systematically analyze the algorithm's performance, we apply our method in human-led imitation learning environment where a robot learns reusable components of the task from short demonstrations to correctly imitate more complicated, longer demonstrations of the same task category. The results suggest that under reasonable amount of noise, our method is capable to capture the reusable structures of tasks and generalize to cope with recursions.
  • Conference paper
    Soh H, Demiris Y, 2012,

    Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process

    , International Joint Conference on Neural Networks, IJCNN, Publisher: IEEE, Pages: 1-8, ISSN: 2161-4393

    In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dynamics and producing predictive distributions (instead of point predictions). Our method can be seen as a combination of the echo state network with a sparse approximation of Gaussian processes (GPs). Extensive experiments on the one-step prediction task on well-known benchmark problems show that OESGP produced statistically superior results to current online ESNs and state-of-the-art regression methods. In addition, we characterise the benefits (and drawbacks) associated with the considered online methods, specifically with regards to the trade-off between computational cost and accuracy. For a high-dimensional action recognition task, we demonstrate that OESGP produces high accuracies comparable to a recently published graphical model, while being fast enough for real-time interactive scenarios.
  • Journal article
    Chatzis SP, Demiris Y, 2012,

    The copula echo state network

    , Pattern Recognition, Vol: 45, Pages: 570-577, ISSN: 0031-3203

    Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple, computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. This paper studies the formulation of a class of copula-based semiparametric models for sequential data modeling, characterized by nonparametric marginal distributions modeled by postulating suitable echo state networks, and parametric copula functions that help capture all the scale-free temporal dependence of the modeled processes. We provide a simple algorithm for the data-driven estimation of the marginal distribution and the copula parameters of our model under the maximum-likelihood framework. We exhibit the merits of our approach by considering a number of applications; as we show, our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs, without significant compromises in the algorithm's computational efficiency.
  • Journal article
    Chatzis SP, Korkinof D, Demiris Y, 2012,

    A Quantum-Statistical Approach Toward Robot Learning by Demonstration

    , IEEE Transactions on Robotics, Vol: 28, Pages: 1371-1381-1371-1381, ISSN: 1941-0468

    Statistical machine learning approaches have been at the epicenter of the ongoing research work in the field of robot learning by demonstration over the past few years. One of the most successful methodologies used for this purpose is a Gaussian mixture regression (GMR). In this paper, we propose an extension of GMR-based learning by demonstration models to incorporate concepts from the field of quantum mechanics. Indeed, conventional GMR models are formulated under the notion that all the observed data points can be assigned to a distinct number of model states (mixture components). In this paper, we reformulate GMR models, introducing some quantum states constructed by superposing conventional GMR states by means of linear combinations. The so-obtained quantum statistics-inspired mixture regression algorithm is subsequently applied to obtain a novel robot learning by demonstration methodology, offering a significantly increased quality of regenerated trajectories for computational costs comparable with currently state-of-the-art trajectory-based robot learning by demonstration approaches. We experimentally demonstrate the efficacy of the proposed approach.
  • Conference paper
    Ros R, Nalin M, Wood R, Baxter P, Looije R, Demiris Y, Belpaeme T, Giusti A, Pozzi Cet al., 2011,

    Child-robot interaction in the wild: Advice to the aspiring experimenter

    , Pages: 335-342

    We present insights gleaned from a series of child-robot interaction experiments carried out in a hospital paediatric department. Our aim here is to share good practice in experimental design and lessons learned about the implementation of systems for social HRI with child users towards application in "the wild", rather than in tightly controlled and constrained laboratory environments: a trade-off between the structures imposed by experimental design and the desire for removal of such constraints that inhibit interaction depth, and hence engagement, requires a careful balance. © 2011 ACM.
  • Conference paper
    Wu Y, Demiris Y, 2011,

    Learning Dynamical Representations of Tools for Tool-Use Recognition

    , International Conference on Robotics and Biomimetics (ROBIO), Publisher: IEEE, Pages: 2664-2669

    We consider the problem of representing and recognising tools, a subset of objects that have special functionality and action patterns. Our proposed framework is based on the biological evidence of hierarchical representation of tools in the region of the human cortex that generates action semantics. It addresses the shortfalls of traditional learning models of object representation applied on tools. To showcase its merits, this framework is implemented as a hybrid model between the Hierarchical Attentive Multiple Models for Execution and Recognition of Actions Architecture (HAMMER) and Hidden Markov Model (HMM) to recognise and describe tools as dynamic patterns at symbolic level. The implemented model is tested and validated on two sets of experiments of 50 human demonstrations each on using 5 different tools. In the experiment with precise and accurate input data, the cross-validation statistics suggest very robust identification of the learned tools. In the experiment with unstructured environment, all errors can be explained systematically.
  • Conference paper
    Chatzis SP, Korkinof D, Demiris Y, 2011,

    The One-Hidden Layer Non-parametric Bayesian Kernel Machine

    , IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

    In this paper, we present a nonparametric Bayesian approach towards one-hidden-layer feedforward neural net- works. Our approach is based on a random selection of the weights of the synapses between the input and the hidden layer neurons, and a Bayesian marginalization over the weights of the connections between the hidden layer neurons and the output neurons, giving rise to a kernel-based nonparametric Bayesian inference procedure for feedforward neural networks. Compared to existing approaches, our method presents a number of advan- tages, with the most significant being: (i) it offers a significant improvement in terms of the obtained generalization capabilities; (ii) being a nonparametric Bayesian learning approach, it entails inference instead of fitting to data, thus resolving the overfitting issues of non-Bayesian approaches; and (iii) it yields a full predictive posterior distribution, thus naturally providing a measure of uncertainty on the generated predictions (expressed by means of the variance of the predictive distribution), without the need of applying computationally intensive methods, e.g., bootstrap. We exhibit the merits of our approach by investigating its application to two difficult multimedia content classification applications: semantic characterization of audio scenes based on content, and yearly song classification, as well as a set of benchmark classification and regression tasks.
  • Conference paper
    Claassens J, Demiris Y, 2011,

    Generalising human demonstration data by identifying affordance symmetries in object interaction trajectories.

    , International Conference on Robotics and Intelligent Systems (IROS), Publisher: IEEE, Pages: 1980-1985

    This paper concerns modelling human hand or tool trajectories when interacting with everyday objects. In these interactions symmetries may be exhibited in portions of the trajectories which can be used to identify task space redundancy. This paper presents a formal description of a set of these symmetries, which we term affordance symmetries, and a method to identify them in multiple demonstration recordings. The approach is robust to arbitrary motion before and after the symmetry artifact and relies only on recorded trajectory data. To illustrate the method's performance two examples are discussed involving two different types of symmetries. An simple illustration of the application of the concept in reproduction planning is also provided.
  • Conference paper
    Kruijff-Korbayová I, Cuayáhuitl H, Kiefer B, Schröder M, Racioppa S, Cosi P, Sommavilla G, Tesser F, Sahli H, Athanasopoulos G, Wang W, Enescu V, Verhelst W, Cañamero L, Beck A, Hiolle A, Ros R, Demiris Yet al., 2011,

    A conversational system for multi-session child-robot interaction with several games

    , Germany, KI-2012: Poster and Demo Track, Pages: 135-139
  • Journal article
    Chatzis SP, Demiris Y, 2011,

    Echo State Gaussian Process

    , IEEE Transactions on Neural Networks, Vol: 22, Pages: 1435-1445, ISSN: 1045-9227

    Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. In this paper, we introduce a novel Bayesian approach toward ESNs, the echo state Gaussian process (ESGP). The ESGP combines the merits of ESNs and Gaussian processes to provide a more robust alternative to conventional reservoir computing networks while also offering a measure of confidence on the generated predictions (in the form of a predictive distribution). We exhibit the merits of our approach in a number of applications, considering both benchmark datasets and real-world applications, where we show that our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs. Additionally, we also show that our method is orders of magnitude more computationally efficient compared to existing Gaussian process-based methods for dynamical data modeling, without compromises in the obtained predictive performance.
  • Conference paper
    Lee K, Demiris Y, 2011,

    Towards incremental learning of task-dependent action sequences using probabilistic parsing

    , Frankfurt, Germany, International Conference on Development and Learning (ICDL), Publisher: IEEE, Pages: 1-6

    We study an incremental process of learning where a set of generic basic actions are used to learn higher-level task-dependent action sequences. A task-dependent action sequence is learned by associating the goal given by a human demonstrator with the task-independent, general-purpose actions in the action repertoire. This process of contextualization is done using probabilistic parsing. We propose stochastic context-free grammars as the representational framework due to its robustness to noise, structural flexibility, and easiness on defining task-independent actions. We demonstrate our implementation on a real-world scenario using a humanoid robot and report implementation issues we had.
  • Conference paper
    Soh H, Demiris Y, 2011,

    Multi-reward policies for medical applications: Anthrax attacks and smart wheelchairs

    , Pages: 471-477

    Medical decisions are often difficult; they involve uncertain information, multiple-objectives and debatable outcomes. In this work, we discuss the application of the multi-reward partially-observable Markov decision process (MR-POMDP) and NSGA2-LS, a hybridised multi-objective evolutionary solver, to two problems in the medical domain: anthrax response and smart-wheelchair control. For the first problem, we use a discrete model and analyse the trade-offs between the best solutions (in the form of finite-state controllers) found by our evolutionary algorithm. For the second, we contribute an extension of our method to the continuous space and optimising recurrent neural networks (RNNs) for use on medical robots such as smart wheelchairs. © 2011 ACM.
  • Conference paper
    Soh H, Demiris Y, 2011,

    Evolving Policies for Multi-Reward Partially Observable Markov Decision Processes (MR-POMDPs)

    , Genetic and Evolutionary Computation Conference (GECCO), Publisher: ACM, Pages: 713-720

    Plans and decisions in many real-world scenarios are made under uncertainty and to satisfy multiple, possibly conflicting, objectives. In this work, we contribute the multi-reward partially-observable Markov decision process (MR-POMDP) as a general modelling framework. To solve MR-POMDPs, we present two hybrid (memetic) multi-objective evolutionary algorithms that generate non-dominated sets of policies (in the form of stochastic finite state controllers). Performance comparisons between the methods on multi-objective problems in robotics (with 2, 3 and 5 objectives), web-advertising (with 3, 4 and 5 objectives) and infectious disease control (with 3 objectives), revealed that memetic variants outperformed their original counterparts. We anticipate that the MR-POMDP along with multi-objective evolutionary solvers will prove useful in a variety of theoretical and real-world applications.
  • Conference paper
    Soh H, Demiris Y, 2011,

    Involving Young Children in the Design of a Safe, Smart Paediatric Wheelchair

    , HRI Pioneers Workshop, Pages: 86-87

    Independent mobility is crucial for a growing child and its loss can severely impact cognitive, emotional and social development. Unfortunately, powered wheelchair provision for young children has been difficult due to safety concerns. But powered mobility need not be unsafe. Risks can be reduced through the use of robotic technology (e.g., obstacle avoidance) and we present a prototype safe smart paediatric wheelchair: the Assistive Robot Transport for Youngsters (ARTY). A core aspect of our work is that we aim to bring ARTY to the field and we discuss the challenges faced when trying to involve children in the development/testing of medical technology. We discuss one preliminary experiment designed as a “Hide-and-Seek” game as a short case study.
  • Conference paper
    Sarabia M, Ros R, Demiris Y, 2011,

    Towards an open-source social middleware for humanoid robots

    , International Conference on Humanoid Robotics, Publisher: IEEE, Pages: 670-675

    Recent examples of robotics middleware including YARP, ROS, and NaoQi, have greatly enhanced the standardisation, interoperability and rapid development of robotics application software. In this paper, we present our research towards an open source middleware to support the development of social robotic applications. In the core of the ability of a robot to interact socially are algorithms to perceive the actions and intentions of a human user. We attempt to provide a computational layer to standardise these algorithms utilising a bioinspired computational architecture known as HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition) and demonstrate the deployment of such layer on two different humanoid platforms, the Nao and iCub robots. We use a dance interaction scenario to demonstrate the utility of the framework.
  • Conference paper
    Ros R, Demiris Y, Baroni I, Nalin Met al., 2011,

    Adapting Robot Behavior to User's Capabilities: A Dance Instruction Study

    , 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Publisher: IEEE, Pages: 235-236, ISSN: 2167-2121
  • Journal article
    Takacs B, Demiris Y, 2010,

    Spectral clustering in multi-agent systems

    , Knowledge and Information Systems, Vol: 25, Pages: 607-622, ISSN: 0219-1377

    We examine the application of spectral clustering for breaking up the behavior of a multi-agent system in space and time into smaller, independent elements. We propose clustering observations of individual entities in order to identify significant changes in the parameter space (like spatial position) and detect temporal alterations of behavior within the same framework. Available knowledge of important interactions (events) between entities is also considered. We describe a novel algorithm utilizing iterative subdivisions where clusters are pre-processed at each step to counter spatial scaling, rotation, replay speed, and varying sampling frequency. A method is presented to balance spatial and temporal segmentation based on the expected group size, and a validity measure is introduced to determine the optimal number of clusters. We demonstrate our results by analyzing the outcomes of computer games and compare our algorithm to K-means and traditional spectral clustering.
  • Conference paper
    Wu Y, Kuvinichkul P, Cheung PYK, Demiris Yet al., 2010,

    Towards Anthropomorphic Robot Thereminist

    , International Conference on Robotics and Biomimetics (ROBIO), Publisher: IEEE, Pages: 235-240

    Theremin is an electronic musical instrument considered to be the most difficult to play which requires the player's hands to have high precision and stability as any position change within proximity of the instrument's antennae can make a difference to the pitch or volume. In a different direction to previous developments of Theremin playing robots, we propose a Humanoid Thereminist System that goes beyond using only one degree of freedom which will open up the possibility for robot to acquire more complex skills, such as aerial fingering and include musical expressions in playing the Theremin. The proposed system consists of two phases, namely calibration phase and playing phase which can be executed independently. During the playing phase, the System takes input from a MIDI file and performs path planning using a combination of minimum energy strategy in joint space and feedback error correction for next playing note. Three experiments have been conducted to evaluate the developed system quantitatively and qualitatively by playing a selection of music files. The experiments have demonstrated that the proposed system can effectively utilise multiple degrees of freedoms while maintaining minimum pitch error margins.
  • Conference paper
    Wu Y, Demiris Y, 2010,

    Hierarchical Learning Approach for One-shot Action Imitation in Humanoid Robots

    , International Conference on Control, Automation, Robotics and Vision (ICARCV), Publisher: IEEE, Pages: 453-458

    We consider the issue of segmenting an action in the learning phase into a logical set of smaller primitives in order to construct a generative model for imitation learning using a hierarchical approach. Our proposed framework, addressing the “how-to” question in imitation, is based on a one-shot imitation learning algorithm. It incorporates segmentation of a demonstrated template into a series of subactions and takes a hierarchical approach to generate the task action by using a finite state machine in a generative way. Two sets of experiments have been conducted to evaluate the performance of the framework, both statistically and in practice, through playing a tic-tac-toe game. The experiments demonstrate that the proposed framework can effectively improve the performance of the one-shot learning algorithm and reduce the size of primitive space, without compromising the learning quality.
  • Conference paper
    Martins MF, Demiris Y, 2010,

    Learning multirobot joint action plans from simultaneous task execution demonstrations.

    , International Conference on Autonomous Agents and Multiagent Systems, Publisher: ACM, Pages: 931-938

    The central problem of designing intelligent robot systems which learn by demonstrations of desired behaviour has been largely studied within the field of robotics. Numerous architectures for action recognition and prediction of intent of a single teacher have been proposed. However, little work has been done addressing how a group of robots can learn by simultaneous demonstrations of multiple teachers.This paper contributes a novel approach for learning multirobot joint action plans from unlabelled data. The robots firstly learn the demonstrated sequence of individual actions using the HAMMER architecture. Subsequently, the group behaviour is segmented over time and space by applying a spatio-temporal clustering algorithm.The experimental results, in which humans teleoperated real robots during a search and rescue task deployment, successfully demonstrated the efficacy of combining action recognition at individual level with group behaviour segmentation, spotting the exact moment when robots must form coalitions to achieve the goal, thus yielding reasonable generation of multirobot joint action plans.
  • Conference paper
    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.
  • Conference paper
    Wu Y, Demiris Y, 2010,

    Towards One Shot Learning by Imitation for Humanoid Robots

    , International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 2889-2894, ISSN: 1050-4729

    Teaching a robot to learn new knowledge is a repetitive and tedious process. In order to accelerate the process, we propose a novel template-based approach for robot arm movement imitation. This algorithm selects a previously observed path demonstrated by a human 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 a combination of 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 those involving forced waypoints. As compared to traditional methodologies, our work require neither extensive training for generalisation nor expensive run-time computation for accuracy. This algorithm has been statistically validated using cross-validation of grasping experiments as well as tested for practical implementation on the iCub humanoid robot for playing the tic-tac-toe game.
  • Conference paper
    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.
  • Conference paper
    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
  • Conference paper
    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.
  • Conference paper
    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
  • Conference paper
    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.

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