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  • Conference paper
    Lane DM, Maurelli F, Kormushev P, Carreras M, Fox M, Kyriakopoulos Ket al., 2015,

    PANDORA - Persistent Autonomy through Learning, Adaptation, Observation and Replanning
  • Conference paper
    Jamisola RS, Kormushev P, Caldwell DG, Ibikunle Fet al., 2015,

    Modular Relative Jacobian for Dual-Arms and the Wrench Transformation Matrix
  • Journal article
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Appearance-based indoor localization: a comparison of patch descriptor performance

    , Pattern Recognition Letters, Vol: 66, Pages: 109-117, ISSN: 1872-7344

    Vision is one of the most important of the senses, and humans use it extensively during navigation. We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry. To do this, we created a database containing over 3 km of video-sequences with ground-truth in the form of distance travelled along different corridors. Using this database, the accuracy of localization—both in terms of knowing which route a user is on—and in terms of position along a certain route, can be evaluated. For each type of descriptor, we also tested different techniques to encode visual structure and to search between journeys to estimate a user’s position. The techniques include single-frame descriptors, those using sequences of frames, and both color and achromatic descriptors. We found that single-frame indexing worked better within this particular dataset. This might be because the motion of the person holding the camera makes the video too dependent on individual steps and motions of one particular journey. Our results suggest that appearance-based information could be an additional source of navigational data indoors, augmenting that provided by, say, radio signal strength indicators (RSSIs). Such visual information could be collected by crowdsourcing low-resolution video feeds, allowing journeys made by different users to be associated with each other, and location to be inferred without requiring explicit mapping. This offers a complementary approach to methods based on simultaneous localization and mapping (SLAM) algorithms.
  • Conference paper
    Deisenroth MP, Ng JW, 2015,

    Distributed Gaussian processes

    , Pages: 1481-1490

    To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or variational parameters. The key idea is to recursively distribute computations to independent computational units and, subsequently, re-combine them to form an overall result. Efficient closed-form inference allows for straightforward parallelisation and distributed computations with a small memory footprint. The rBCM is independent of the computational graph and can be used on heterogeneous computing infrastructures, ranging from laptops to clusters. With sufficient computing resources our distributed GP model can handle arbitrarily large data sets.
  • Journal article
    Takano W, Asfour T, Kormushev P, 2015,

    Special Issue on Humanoid Robotics

    , Advanced Robotics, Vol: 29
  • Conference paper
    Calandra R, Ivaldi S, Deisenroth MP, Rueckert E, Peters Jet al., 2015,

    Learning Inverse Dynamics Models with Contacts

    , 2015 IEEE International Conference on Robotics and Automation (ICRA)
  • Conference paper
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Associating Locations Between Indoor Journeys from Wearable Cameras

    , 13th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 29-44, ISSN: 0302-9743
  • Conference paper
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Indoor Localisation with Regression Networks and Place Cell Models.

    , Publisher: BMVA Press, Pages: 147.1-147.1
  • Journal article
    Bimbo J, Kormushev P, Althoefer K, Liu Het al., 2015,

    Global Estimation of an Object’s Pose Using Tactile Sensing

    , Advanced Robotics, Vol: 29
  • Conference paper
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2014,

    Multi-Objective Reinforcement Learning for AUV Thruster Failure Recovery

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