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  • JOURNAL ARTICLE
    Chang HJ, Demiris Y, 2018,

    Highly Articulated Kinematic Structure Estimation Combining Motion and Skeleton Information

    , IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 40, Pages: 2165-2179, ISSN: 0162-8828
  • CONFERENCE PAPER
    Choi J, Chang HJ, Fischer T, Yun S, Lee K, Jeong J, Demiris Y, Choi JYet al., 2018,

    Context-aware Deep Feature Compression for High-speed Visual Tracking

    We propose a new context-aware correlation filter based tracking framework toachieve both high computational speed and state-of-the-art performance amongreal-time trackers. The major contribution to the high computational speed liesin the proposed deep feature compression that is achieved by a context-awarescheme utilizing multiple expert auto-encoders; a context in our frameworkrefers to the coarse category of the tracking target according to appearancepatterns. In the pre-training phase, one expert auto-encoder is trained percategory. In the tracking phase, the best expert auto-encoder is selected for agiven target, and only this auto-encoder is used. To achieve high trackingperformance with the compressed feature map, we introduce extrinsic denoisingprocesses and a new orthogonality loss term for pre-training and fine-tuning ofthe expert auto-encoders. We validate the proposed context-aware frameworkthrough a number of experiments, where our method achieves a comparableperformance to state-of-the-art trackers which cannot run in real-time, whilerunning at a significantly fast speed of over 100 fps.

  • JOURNAL ARTICLE
    Cully A, Demiris Y, 2018,

    Quality and Diversity Optimization: A Unifying Modular Framework

    , IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, Vol: 22, Pages: 245-259, ISSN: 1089-778X
  • CONFERENCE PAPER
    Fischer T, Chang HJ, Demiris Y, 2018,

    RT-GENE: Real-time eye gaze estimation in natural environments

    , Pages: 339-357, ISSN: 0302-9743

    © Springer Nature Switzerland AG 2018. In this work, we consider the problem of robust gaze estimation in natural environments. Large camera-to-subject distances and high variations in head pose and eye gaze angles are common in such environments. This leads to two main shortfalls in state-of-the-art methods for gaze estimation: hindered ground truth gaze annotation and diminished gaze estimation accuracy as image resolution decreases with distance. We first record a novel dataset of varied gaze and head pose images in a natural environment, addressing the issue of ground truth annotation by measuring head pose using a motion capture system and eye gaze using mobile eyetracking glasses. We apply semantic image inpainting to the area covered by the glasses to bridge the gap between training and testing images by removing the obtrusiveness of the glasses. We also present a new real-time algorithm involving appearance-based deep convolutional neural networks with increased capacity to cope with the diverse images in the new dataset. Experiments with this network architecture are conducted on a number of diverse eye-gaze datasets including our own, and in cross dataset evaluations. We demonstrate state-of-the-art performance in terms of estimation accuracy in all experiments, and the architecture performs well even on lower resolution images.

  • CONFERENCE PAPER
    Fischer T, Demiris Y, 2018,

    A computational model for embodied visual perspective taking: from physical movements to mental simulation

    , Vision Meets Cognition Workshop at CVPR 2018

    To understand people and their intentions, humans have developed the ability to imagine their surroundings from another visual point of view. This cognitive ability is called perspective taking and has been shown to be essential in child development and social interactions. However, the precise cognitive mechanisms underlying perspective taking remain to be fully understood. Here we present a computa- tional model that implements perspective taking as a mental simulation of the physical movements required to step into the other point of view. The visual percept after each mental simulation step is estimated using a set of forward models. Based on our experimental results, we propose that a visual attention mechanism explains the response times reported in human visual perspective taking experiments. The model is also able to generate several testable predictions to be explored in further neurophysiological studies.

  • JOURNAL ARTICLE
    Fischer T, Puigbo J-Y, Camilleri D, Nguyen PDH, Moulin-Frier C, Lallee S, Metta G, Prescott TJ, Demiris Y, Verschure PFMJet al., 2018,

    iCub-HRI: A Software Framework for Complex Human-Robot Interaction Scenarios on the iCub Humanoid Robot

    , FRONTIERS IN ROBOTICS AND AI, Vol: 5, ISSN: 2296-9144
  • CONFERENCE PAPER
    Nguyen P, Fischer T, Chang HJ, Pattacini U, Metta G, Demiris Yet al., 2018,

    Transferring visuomotor learning from simulation to the real world for robotics manipulation tasks

    , IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE

    Hand-eye coordination is a requirement for many manipulation tasks including grasping and reaching. However, accurate hand-eye coordination has shown to be especially difficult to achieve in complex robots like the iCub humanoid. In this work, we solve the hand-eye coordination task using a visuomotor deep neural network predictor that estimates the arm's joint configuration given a stereo image pair of the arm and the underlying head configuration. As there are various unavoidable sources of sensing error on the physical robot, we train the predictor on images obtained from simulation. The images from simulation were modified to look realistic using an image-to-image translation approach. In various experiments, we first show that the visuomotor predictor provides accurate joint estimates of the iCub's hand in simulation. We then show that the predictor can be used to obtain the systematic error of the robot's joint measurements on the physical iCub robot. We demonstrate that a calibrator can be designed to automatically compensate this error. Finally, we validate that this enables accurate reaching of objects while circumventing manual fine-calibration of the robot.

  • CONFERENCE PAPER
    Zolotas M, Elsdon J, Demiris Y, 2018,

    Head-mounted augmented reality for explainable robotic wheelchair assistance

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE

    Robotic wheelchairs with built-in assistive fea-tures, such as shared control, are an emerging means ofproviding independent mobility to severely disabled individuals.However, patients often struggle to build a mental model oftheir wheelchair’s behaviour under different environmentalconditions. Motivated by the desire to help users bridge thisgap in perception, we propose a novel augmented realitysystem using a Microsoft Hololens as a head-mounted aid forwheelchair navigation. The system displays visual feedback tothe wearer as a way of explaining the underlying dynamicsof the wheelchair’s shared controller and its predicted futurestates. To investigate the influence of different interface designoptions, a pilot study was also conducted. We evaluated theacceptance rate and learning curve of an immersive wheelchairtraining regime, revealing preliminary insights into the potentialbeneficial and adverse nature of different augmented realitycues for assistive navigation. In particular, we demonstrate thatcare should be taken in the presentation of information, witheffort-reducing cues for augmented information acquisition (forexample, a rear-view display) being the most appreciated.

  • JOURNAL ARTICLE
    Chang HJ, Fischer T, Petit M, Zambelli M, Demiris Yet al., 2017,

    Learning Kinematic Structure Correspondences Using Multi-Order Similarities.

    , IEEE Trans Pattern Anal Mach Intell

    We present a novel framework for finding the kinematic structure correspondences between two articulated objects in videos via hypergraph matching. In contrast to appearance and graph alignment based matching methods, which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Thus our method allows matching the structure of objects which have similar topologies or motions, or a combination of the two. Our main contributions are summarised as follows: (i)casting the kinematic structure correspondence problem into a hypergraph matching problem by incorporating multi-order similarities with normalising weights, (ii)introducing a structural topology similarity measure by aggregating topology constrained subgraph isomorphisms, (iii)measuring kinematic correlations between pairwise nodes, and (iv)proposing a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on synthetic and real data, showing that various other recent and state of the art methods are outperformed. Our method is not limited to a specific application nor sensor, and can be used as building block in applications such as action recognition, human motion retargeting to robots, and articulated object manipulation.

  • CONFERENCE PAPER
    Choi J, Chang HJ, Yun S, Fischer T, Demiris Y, Choi JYet al., 2017,

    Attentional correlation filter network for adaptive visual tracking

    , IEEE Conference on Computer Vision and Pattern Recognition, Publisher: IEEE, ISSN: 1063-6919

    We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional Correlation Filter Network which allows adaptive tracking of dynamic targets. (ii) Utilising an attentional network which shifts the attention to the best candidate modules, as well as predicting the estimated accuracy of currently inactive modules. (iii) Enlarging the variety of correlation filters which cover target drift, blurriness, occlusion, scale changes, and flexible aspect ratio. (iv) Validating the robustness and efficiency of the attentional mechanism for visual tracking through a number of experiments. Our method achieves similar performance to non real-time trackers, and state-of-the-art performance amongst real-time trackers.

  • CONFERENCE PAPER
    Elsdon J, Demiris Y, 2017,

    Assisted painting of 3D structures using shared control with a hand-held robot

    , IEEE International Conference on Robotics and Automation, Publisher: IEEE

    Abstract— We present a shared control method of painting3D geometries, using a handheld robot which has a singleautonomously controlled degree of freedom. The user scansthe robot near to the desired painting location, the singlemovement axis moves the spray head to achieve the requiredpaint distribution. A simultaneous simulation of the sprayingprocedure is performed, giving an open loop approximationof the current state of the painting. An online prediction ofthe best path for the spray nozzle actuation is calculated ina receding horizon fashion. This is calculated by producing amap of the paint required in the 2D space defined by nozzleposition on the gantry and the time into the future. A directedgraph then extracts its edge weights from this paint density mapand Dijkstra’s algorithm is then used to find the candidate forthe most effective path. Due to the heavy parallelisation of thisapproach and the majority of the calculations taking place on aGPU we can run the prediction loop in 32.6ms for a predictionhorizon of 1 second, this approach is computationally efficient,outperforming a greedy algorithm. The path chosen by theproposed method on average chooses a path in the top 15%of all paths as calculated by exhaustive testing. This approachenables development of real time path planning for assistedspray painting onto complicated 3D geometries. This methodcould be applied to applications such as assistive painting forpeople with disabilities, or accurate placement of liquid whenlarge scale positioning of the head is too expensive.

  • JOURNAL ARTICLE
    Georgiou T, Demiris Y, 2017,

    Adaptive user modelling in car racing games using behavioural and physiological data

    , USER MODELING AND USER-ADAPTED INTERACTION, Vol: 27, Pages: 267-311, ISSN: 0924-1868
  • JOURNAL ARTICLE
    Korkinof D, Demiris Y, 2017,

    Multi-task and multi-kernel Gaussian process dynamical systems

    , PATTERN RECOGNITION, Vol: 66, Pages: 190-201, ISSN: 0031-3203
  • JOURNAL ARTICLE
    Moulin-Frier C, Fischer T, Petit M, Pointeau G, Puigbo JY, Pattacini U, Low SC, Camilleri D, Nguyen P, Hoffmann M, Chang HJ, Zambelli M, Mealier AL, Damianou A, Metta G, Prescott TJ, Demiris Y, Dominey PF, Verschure PFMJet al., 2017,

    DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

    , IEEE Transactions on Cognitive and Developmental Systems, ISSN: 2379-8920

    This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

  • CONFERENCE PAPER
    Yoo Y, Yun S, Chang HJ, Demiris Y, Choi JYet al., 2017,

    Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold

    , 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 2943-2952, ISSN: 1063-6919
  • JOURNAL ARTICLE
    Zambelli M, Demiris Y, 2017,

    Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations

    , IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, Vol: 9, Pages: 113-126, ISSN: 2379-8920
  • CONFERENCE PAPER
    Zhang F, Cully A, Demiris Y, 2017,

    Personalized Robot-assisted Dressing using User Modeling in Latent Spaces

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3603-3610, ISSN: 2153-0858
  • CONFERENCE PAPER
    Chang HJ, Fischer T, Petit M, Zambelli M, Demiris Yet al., 2016,

    Kinematic structure correspondences via hypergraph matching

    , IEEE Conference on Computer Vision and Pattern Recognition, Publisher: IEEE, ISSN: 1063-6919

    In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem, incorporating multi-order similarities with normalising weights, (ii) a structural topology similarity measure by a new topology constrained subgraph isomorphism aggregation, (iii) a kinematic correlation measure between pairwise nodes, and (iv) a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on complex articulated synthetic and real data.

  • CONFERENCE PAPER
    Choi J, Chang HJ, Jeong J, Demiris Y, Choi JYet al., 2016,

    Visual Tracking Using Attention-Modulated Disintegration and Integration

    , 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 4321-4330, ISSN: 1063-6919
  • CONFERENCE PAPER
    Fischer T, Demiris Y, 2016,

    Markerless Perspective Taking for Humanoid Robots in Unconstrained Environments

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 3309-3316, ISSN: 1050-4729
  • CONFERENCE PAPER
    Gao Y, Chang HJ, Demiris Y, 2016,

    Personalised assistive dressing by humanoid robots using multi-modal information

    , Workshop on Human-Robot Interfaces for Enhanced Physical Interactions at ICRA

    In this paper, we present an approach to enable a humanoid robot to provide personalised dressing assistance for human users using multi-modal information. A depth sensor is mounted on top of the robot to provide visual information, and the robot end effectors are equipped with force sensors to provide haptic information. We use visual information to model the movement range of human upper-body parts. The robot plans the dressing motions using the movement rangemodels and real-time human pose. During assistive dressing, the force sensors are used to detect external force resistances. We present how the robot locally adjusts its motions based on the detected forces. In the experiments we show that the robot can assist human to wear a sleeveless jacket while reacting tothe force resistances.

  • CONFERENCE PAPER
    Gao Y, Chang HJ, Demiris Y, 2016,

    Iterative Path Optimisation for Personalised Dressing Assistance using Vision and Force Information

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 4398-4403
  • CONFERENCE PAPER
    Georgiou T, Demiris Y, 2016,

    Personalised Track Design in Car Racing Games

    , IEEE Conference on Computational Intelligence and Games (CIG), Publisher: IEEE, ISSN: 2325-4270
  • CONFERENCE PAPER
    Petit M, Demiris Y, 2016,

    Hierarchical Action Learning by Instruction Through Interactive Grounding of Body Parts and Proto-actions

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 3375-3382, ISSN: 1050-4729
  • JOURNAL ARTICLE
    Petit M, Fischer T, Demiris Y, 2016,

    Lifelong Augmentation of Multi-Modal Streaming Autobiographical Memories

    , IEEE Transactions on Cognitive and Developmental Systems, Vol: 8, Pages: 201-213, ISSN: 2379-8920

    Robot systems that interact with humans over extended periods of time will benefit from storing and recalling large amounts of accumulated sensorimotor and interaction data. We provide a principled framework for the cumulative organisation of streaming autobiographical data so that data can be continuously processed and augmented as the processing and reasoning abilities of the agent develop and further interactions with humans take place. As an example, we show how a kinematic structure learning algorithm reasons a-posteriori about the skeleton of a human hand. A partner can be asked to provide feedback about the augmented memories, which can in turn be supplied to the reasoning processes in order to adapt their parameters. We employ active, multi-modal remembering, so the robot as well as humans can gain insights of both the original and augmented memories. Our framework is capable of storing discrete and continuous data in real-time. The data can cover multiple modalities and several layers of abstraction (e.g. from raw sound signals over sentences to extracted meanings). We show a typical interaction with a human partner using an iCub humanoid robot. The framework is implemented in a platform-independent manner. In particular, we validate its multi platform capabilities using the iCub, Baxter and NAO robots. We also provide an interface to cloud based services, which allow automatic annotation of episodes. Our framework is geared towards the developmental robotics community, as it 1) provides a variety of interfaces for other modules, 2) unifies previous works on autobiographical memory, and 3) is licensed as open source software.

  • CONFERENCE PAPER
    Petit M, Fischer T, Demiris Y, 2016,

    Towards the Emergence of Procedural Memories from Lifelong Multi-Modal Streaming Memories for Cognitive Robots

    , Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics at IEEE/RSJ IROS

    Various research topics are emerging as the demand for intelligent lifelong interactions between robot and humans increases. Among them, we can find the examination of persistent storage, the continuous unsupervised annotation of memories and the usage of data at high-frequency over long periods of time. We recently proposed a lifelong autobiographical memory architecture tackling some of these challenges, allowing the iCub humanoid robot to 1) create new memories for both actions that are self-executed and observed from humans, 2) continuously annotate these actions in an unsupervised manner, and 3) use reasoning modules to augment these memories a-posteriori. In this paper, we present a reasoning algorithm which generalises the robots’ understanding of actions by finding the point of commonalities with the former ones. In particular, we generated and labelled templates of pointing actions in different directions. This represents a first step towards the emergence of a procedural memory within a long-term autobiographical memory framework for robots.

  • CONFERENCE PAPER
    Zambelli M, Demiris Y, 2016,

    Multimodal Imitation using Self-learned Sensorimotor Representations

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3953-3958
  • CONFERENCE PAPER
    Zambelli M, Fischer T, Petit M, Chang HJ, Cully A, Demiris Yet al., 2016,

    Towards Anchoring Self-Learned Representations to Those of Other Agents

    , Workshop on Bio-inspired Social Robot Learning in Home Scenarios IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: Institute of Electrical and Electronics Engineers (IEEE)

    In the future, robots will support humans in their every day activities. One particular challenge that robots will face is understanding and reasoning about the actions of other agents in order to cooperate effectively with humans. We propose to tackle this using a developmental framework, where the robot incrementally acquires knowledge, and in particular 1) self-learns a mapping between motor commands and sensory consequences, 2) rapidly acquires primitives and complex actions by verbal descriptions and instructions from a human partner, 3) discoverscorrespondences between the robots body and other articulated objects and agents, and 4) employs these correspondences to transfer the knowledge acquired from the robots point of view to the viewpoint of the other agent. We show that our approach requires very little a-priori knowledge to achieve imitation learning, to find correspondent body parts of humans, and allows taking the perspective of another agent. This represents a step towards the emergence of a mirror neuron like system based on self-learned representations.

  • CONFERENCE PAPER
    Chang HJ, Demiris Y, 2015,

    Unsupervised Learning of Complex Articulated Kinematic Structures combining Motion and Skeleton Information

    , IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 3138-3146, ISSN: 1063-6919
  • CONFERENCE PAPER
    Gao Y, Chang HJ, Demiris Y, 2015,

    User Modelling for Personalised Dressing Assistance by Humanoid Robots

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 1840-1845, ISSN: 2153-0858
  • CONFERENCE PAPER
    Georgiou T, Demiris Y, 2015,

    Predicting car states through learned models of vehicle dynamics and user behaviours

    , Intelligent Vehicles Symposium (IV), Publisher: IEEE, Pages: 1240-1245

    The ability to predict forthcoming car states is crucial for the development of smart assistance systems. Forthcoming car states do not only depend on vehicle dynamics but also on user behaviour. In this paper, we describe a novel prediction methodology by combining information from both sources - vehicle and user - using Gaussian Processes. We then apply this method in the context of high speed car racing. Results show that the forthcoming position and speed of the car can be predicted with low Root Mean Square Error through the trained model.

  • CONFERENCE PAPER
    Kormushev P, Demiris Y, Caldwell DG, 2015,

    Encoderless Position Control of a Two-Link Robot Manipulator

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 943-949, ISSN: 1050-4729
  • CONFERENCE PAPER
    Kormushev P, Demiris Y, Caldwell DG, 2015,

    Kinematic-free Position Control of a 2-DOF Planar Robot Arm

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 5518-5525, ISSN: 2153-0858
  • CONFERENCE PAPER
    Kucukyilmaz A, Demiris Y, 2015,

    One-shot assistance estimation from expert demonstrations for a shared control wheelchair system

    , International Symposium on Robot and Human Interactive Communication (RO-MAN), Publisher: IEEE, Pages: 438-443

    An emerging research problem in the field of assistive robotics is the design of methodologies that allow robots to provide human-like assistance to the users. Especially within the rehabilitation domain, a grand challenge is to program a robot to mimic the operation of an occupational therapist, intervening with the user when necessary so as to improve the therapeutic power of the assistive robotic system. We propose a method to estimate assistance policies from expert demonstrations to present human-like intervention during navigation in a powered wheelchair setup. For this purpose, we constructed a setting, where a human offers assistance to the user over a haptic shared control system. The robot learns from human assistance demonstrations while the user is actively driving the wheelchair in an unconstrained environment. We train a Gaussian process regression model to learn assistance commands given past and current actions of the user and the state of the environment. The results indicate that the model can estimate human assistance after only a single demonstration, i.e. in one-shot, so that the robot can help the user by selecting the appropriate assistance in a human-like fashion.

  • JOURNAL ARTICLE
    Lee K, Ognibene D, Chang HJ, Kim T-K, Demiris Yet al., 2015,

    STARE: Spatio-Temporal Attention Relocation for Multiple Structured Activities Detection

    , IEEE TRANSACTIONS ON IMAGE PROCESSING, Vol: 24, ISSN: 1057-7149
  • CONFERENCE PAPER
    Sarabia M, Lee K, Demiris Y, 2015,

    Towards a Synchronised Grammars Framework for Adaptive Musical Human-Robot Collaboration

    , IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Publisher: IEEE, Pages: 715-721

    We present an adaptive musical collaboration framework for interaction between a human and a robot. The aim of our work is to develop a system that receives feedback from the user in real time and learns the music progression style of the user over time. To tackle this problem, we represent a song as a hierarchically structured sequence of music primitives. By exploiting the sequential constraints of these primitives inferred from the structural information combined with user feedback, we show that a robot can play music in accordance with the user’s anticipated actions. We use Stochastic Context-Free Grammars augmented with the knowledge of the learnt user’s preferences.We provide synthetic experiments as well as a pilot study with a Baxter robot and a tangible music table. The synthetic results show the synchronisation and adaptivity features of our framework and the pilot study suggest these are applicable to create an effective musical collaboration experience.

  • JOURNAL ARTICLE
    Soh H, Demiris Y, 2015,

    Spatio-Temporal Learning With the Online Finite and Infinite Echo-State Gaussian Processes

    , IEEE Transactions on Neural Networks and Learning Systems, Vol: 26, Pages: 522-536, ISSN: 2162-237X

    Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair.

  • CONFERENCE PAPER
    Zambelli M, Demiris Y, 2015,

    Online Ensemble Learning of Sensorimotor Contingencies

    , Workshop on Sensorimotor Contingencies For Robotics at IROS

    Forward models play a key role in cognitive agents by providing predictions of the sensory consequences of motor commands, also known as sensorimotor contingencies (SMCs). In continuously evolving environments, the ability to anticipate is fundamental in distinguishing cognitive from reactive agents, and it is particularly relevant for autonomous robots, that must be able to adapt their models in an online manner. Online learning skills, high accuracy of the forward models and multiple-step-ahead predictions are needed to enhance the robots’ anticipation capabilities. We propose an online heterogeneous ensemble learning method for building accurate forward models of SMCs relating motor commands to effects in robots’ sensorimotor system, in particular considering proprioception and vision. Our method achieves up to 98% higher accuracy both in short and long term predictions, compared to single predictors and other online and offline homogeneous ensembles. This method is validated on two different humanoid robots, namely the iCub and the Baxter.

  • JOURNAL ARTICLE
    Demiris Y, Aziz-Zadeh L, Bonaiuto J, 2014,

    Information Processing in the Mirror Neuron System in Primates and Machines

    , Neuroinformatics, Vol: 12, Pages: 63-91, ISSN: 1539-2791

    The mirror neuron system in primates matches observations of actions with the motor representations used for their execution, and is a topic of intense research and debate in biological and computational disciplines. In robotics, models of this system have been used for enabling robots to imitate and learn how to perform tasks from human demonstrations. Yet, existing computational and robotic models of these systems are found in multiple levels of description, and although some models offer plausible explanations and testable predictions, the difference in the granularity of the experimental setups, methodologies, computational structures and selected modeled data make principled meta-analyses, common in other fields, difficult. In this paper, we adopt an interdisciplinary approach, using the BODB integrated environment in order to bring together several different but complementary computational models, by functionally decomposing them into brain operating principles (BOPs) which each capture a limited subset of the model’s functionality. We then explore links from these BOPs to neuroimaging and neurophysiological data in order to pinpoint complementary and conflicting explanations and compare predictions against selected sets of neurobiological data. The results of this comparison are used to interpret mirror system neuroimaging results in terms of neural network activity, evaluate the biological plausibility of mirror system models, and suggest new experiments that can shed light on the neural basis of mirror systems.

  • JOURNAL ARTICLE
    Ros R, Baroni I, Demiris Y, 2014,

    Adaptive human-robot interaction in sensorimotor task instruction: From human to robot dance tutors

    , Robotics and Autonomous Systems, Vol: 62, Pages: 707-720, ISSN: 1872-793X

    We explore the potential for humanoid robots to interact with children in a dance activity. In this context, the robot plays the role of an instructor to guide the child through several dance moves to learn a dance phrase. We participated in 30 dance sessions in schools to study human–human interaction between children and a human dance teacher, and to identify the applied methodologies. Based on the strategies observed, both social and task-dependent, we implemented a robotic system capable of autonomously instructing dance sequences to children while displaying basic social cues to engage the child in the task. Experiments were performed in a hospital with the Nao robot interacting with 12 children through multiple encounters, when possible (18 sessions, 236 min). Observational analysis through video recordings and survey evaluations were used to assess the quality of interaction. Moreover, we introduce an involvement measure based on the aggregation of observed behavioral cues to assess the level of interest in the interaction through time. The analysis revealed high levels of involvement, while highlighting the need for further research into social engagement and adaptation with robots over repeated sessions.

  • CONFERENCE PAPER
    Ros R, Coninx A, Demiris Y, Patsis G, Enescu V, Sahli Het al., 2014,

    Behavioral Accommodation towards a Dance Robot Tutor

    , International Conference on Human-Robot Interaction, Publisher: ACM/IEEE, Pages: 278-279

    We report first results on children adaptive behavior towards a dance tutoring robot. We can observe that children behavior rapidly evolves through few sessions in order to accommodate with the robotic tutor rhythm and instructions.

  • JOURNAL ARTICLE
    Soh H, Demiris Y, 2014,

    Incrementally Learning Objects by Touch: Online Discriminative and Generative Models for Tactile-Based Recognition

    , IEEE Transactions on Haptics, Vol: 7, Pages: 512-525, ISSN: 1939-1412

    Human beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based object recognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/ palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate “early” classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches.

  • CONFERENCE PAPER
    Su Y, Dong W, Wu Y, Du Z, Demiris Yet al., 2014,

    Increasing the Accuracy and the Repeatability of Position Control for Micromanipulations Using Heteroscedastic Gaussian Processes

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 4692-4698, ISSN: 1050-4729
  • JOURNAL ARTICLE
    Wu Y, Su Y, Demiris Y, 2014,

    A morphable template framework for robot learning by demonstration: Integrating one-shot and incremental learning approaches

    , Robotics and Autonomous Systems, Vol: 62, Pages: 1517-1530

    Robot learning by demonstration is key to bringing robots into daily social environments to interact with and learn from human and other agents. However, teaching a robot to acquire new knowledge is a tedious and repetitive process and often restrictive to a specific setup of the environment. We propose a template-based learning framework for robot learning by demonstration to address both generalisation and adaptability. This novel framework is based upon a one-shot learning model integrated with spectral clustering and an online learning model to learn and adapt actions in similar scenarios. A set of statistical experiments is used to benchmark the framework components and shows that this approach requires no extensive training for generalisation and can adapt to environmental changes flexibly. Two real-world applications of an iCub humanoid robot playing the tic-tac-toe game and soldering a circuit board are used to demonstrate the relative merits of the framework.

  • 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.

  • 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.

  • 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
    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.

  • JOURNAL ARTICLE
    Lee K, Su Y, Kim T-K, Demiris Yet al., 2013,

    A syntactic approach to robot imitation learning using probabilistic activity grammars

    , ROBOTICS AND AUTONOMOUS SYSTEMS, Vol: 61, Pages: 1323-1334, ISSN: 0921-8890
  • 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.

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