Below is a list of all relevant publications authored by Robotics Forum members.

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  • Conference paper
    Nica A, Vespa E, González de Aledo P, Kelly PHJet al., 2018,

    Investigating automatic vectorization for real-time 3D scene understanding

    Simultaneous Localization And Mapping (SLAM) is the problem of building a representation of a geometric space while simultaneously estimating the observer’s location within the space. While this seems to be a chicken-and-egg problem, several algorithms have appeared in the last decades that approximately and iteratively solve this problem. SLAM algorithms are tailored to the available resources, hence aimed at balancing the precision of the map with the constraints that the computational platform imposes and the desire to obtain real-time results. Working with KinectFusion, an established SLAM implementation, we explore in this work the vectorization opportunities present in this scenario, with the goal of using the CPU to its full potential. Using ISPC, an automatic vectorization tool, we produce a partially vectorized version of KinectFusion. Along the way we explore a number of optimization strategies, among which tiling to exploit ray-coherence and outer loop vectorization, obtaining up to 4x speed-up over the baseline on an 8-wide vector machine.

  • Conference paper
    Escribano Macias J, Angeloudis P, Ochieng W, 2018,

    AIAA Integrated Trajectory-Location-Routing for Rapid Humanitarian Deliveries using Unmanned Aerial Vehicles

    , 2018 Aviation Technology, Integration, and Operations Conference
  • Conference paper
    Avila Rencoret FB, Mylonas GP, Elson D, 2018,

    Robotic Wide-Field Optical Biopsy Imaging For Flexible Endoscopy

    , 26th International Congress of the European Association for Endoscopic Surgery (EAES)
  • Conference paper
    Tavakoli A, Pardo F, Kormushev P, 2018,

    Action branching architectures for deep reinforcement learning

    , AAAI 2018, Publisher: AAAI

    Discrete-action algorithms have been central to numerousrecent successes of deep reinforcement learning. However,applying these algorithms to high-dimensional action tasksrequires tackling the combinatorial increase of the numberof possible actions with the number of action dimensions.This problem is further exacerbated for continuous-actiontasks that require fine control of actions via discretization.In this paper, we propose a novel neural architecture fea-turing a shared decision module followed by several net-workbranches, one for each action dimension. This approachachieves a linear increase of the number of network outputswith the number of degrees of freedom by allowing a level ofindependence for each individual action dimension. To illus-trate the approach, we present a novel agent, called Branch-ing Dueling Q-Network (BDQ), as a branching variant ofthe Dueling Double Deep Q-Network (Dueling DDQN). Weevaluate the performance of our agent on a set of challeng-ing continuous control tasks. The empirical results show thatthe proposed agent scales gracefully to environments with in-creasing action dimensionality and indicate the significanceof the shared decision module in coordination of the dis-tributed action branches. Furthermore, we show that the pro-posed agent performs competitively against a state-of-the-art continuous control algorithm, Deep Deterministic PolicyGradient (DDPG).

  • Conference paper
    Elson D, Avila Rencoret F, Mylonas G, 2018,

    Robotic Wide-Field Optical Biopsy Imaging for Flexible Endoscopy (Gerhard Buess Technology Award)

    , 26th Annual International EAES Congress
  • Conference paper
    Zhao M, Oude Vrielink T, Elson D, Mylonas Get al., 2018,

    Endoscopic TORS-CYCLOPS: A Novel Cable-driven Parallel Robot for Transoral Laser Surgery

    , 26th Annual International EAES Congress
  • Conference paper
    Zhang F, Cully A, Demiris YIANNIS, 2017,

    Personalized Robot-assisted Dressing using User Modeling in Latent Spaces

    , 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, ISSN: 2153-0866

    Robots have the potential to provide tremendous support to disabled and elderly people in their everyday tasks, such as dressing. Many recent studies on robotic dressing assistance usually view dressing as a trajectory planning problem. However, the user movements during the dressing process are rarely taken into account, which often leads to the failures of the planned trajectory and may put the user at risk. The main difficulty of taking user movements into account is caused by severe occlusions created by the robot, the user, and the clothes during the dressing process, which prevent vision sensors from accurately detecting the postures of the user in real time. In this paper, we address this problem by introducing an approach that allows the robot to automatically adapt its motion according to the force applied on the robot's gripper caused by user movements. There are two main contributions introduced in this paper: 1) the use of a hierarchical multi-task control strategy to automatically adapt the robot motion and minimize the force applied between the user and the robot caused by user movements; 2) the online update of the dressing trajectory based on the user movement limitations modeled with the Gaussian Process Latent Variable Model in a latent space, and the density information extracted from such latent space. The combination of these two contributions leads to a personalized dressing assistance that can cope with unpredicted user movements during the dressing while constantly minimizing the force that the robot may apply on the user. The experimental results demonstrate that the proposed method allows the Baxter humanoid robot to provide personalized dressing assistance for human users with simulated upper-body impairments.

  • Conference paper
    Kanajar P, Caldwell DG, Kormushev P, 2017,

    Climbing over large obstacles with a humanoid robot via multi-contact motion planning

    , IEEE RO-MAN 2017: 26th IEEE International Symposium on Robot and Human Interactive Communication, Publisher: IEEE, Pages: 1202-1209

    Incremental progress in humanoid robot locomotion over the years has achieved important capabilities such as navigation over flat or uneven terrain, stepping over small obstacles and climbing stairs. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-body contact motion planning guided through physical human demonstrations. Our goal is to make the humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the whole-body motion and multi-body contacts including both the upper and lower body limbs. The proposed motion planning algorithm is applied to a challenging task of climbing over a large obstacle. We demonstrate successful execution of the climbing task in simulation using our multi-contact motion planning algorithm initialized via a transfer from real-world human demonstrations of the task and further optimized.

  • Conference paper
    Tavakoli A, Pardo F, Kormushev P, 2017,

    Action Branching Architectures for Deep Reinforcement Learning

    , Deep Reinforcement Learning Symposium, 31st Conference on Neural Information Processing Systems (NIPS 2017)
  • Conference paper
    Rakicevic N, Kormushev P, 2017,

    Efficient Robot Task Learning and Transfer via Informed Search in Movement Parameter Space

    , Workshop on Acting and Interacting in the Real World: Challenges in Robot Learning, 31st Conference on Neural Information Processing Systems (NIPS 2017)
  • Conference paper
    Yoo YJ, Chang H, Yun S, Demiris Y, Choi JYet al., 2017,

    Variational autoencoded regression: high dimensional regression of visual data on complex manifold

    , IEEE Conference on Computer Vision and Pattern Recognition, Publisher: IEEE, Pages: 2943-2952

    This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression methods, the proposed method focuses on the case where output responses are on a complex high dimensional manifold, such as images. Our contributions are summarized as follows: (i) A new regression method estimating high dimensional image responses, which is not handled by existing regression algorithms, is proposed. (ii) The proposed regression method introduces a strategy to learn the latent space as well as the encoder and decoder so that the result of the regressed response in the latent space coincide with the corresponding response in the data space. (iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework. We demonstrate the robustness and effectiveness of our method through a number of experiments on various visual data regression problems.

  • 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.

  • Journal article
    Palomeras N, Carrera A, Hurtós N, Karras GC, Bechlioulis CP, Cashmore M, Magazzeni D, Long D, Fox M, Kyriakopoulos KJ, Kormushev P, Salvi J, Carreras Met al., 2016,

    Toward persistent autonomous intervention in a subsea panel

    , Autonomous Robots, Vol: 40, Pages: 1279-1306
  • Journal article
    Jamisola RS, Kormushev P, Roberts RG, Caldwell DGet al., 2016,

    Task-Space Modular Dynamics for Dual-Arms Expressed through a Relative Jacobian

    , Journal of Intelligent & Robotic Systems, Pages: 1-14, ISSN: 1573-0409
  • Book chapter
    Kormushev P, Ahmadzadeh SR, 2016,

    Robot Learning for Persistent Autonomy

    , Handling Uncertainty and Networked Structure in Robot Control, Editors: Busoniu, Tamás, Publisher: Springer International Publishing, Pages: 3-28, ISBN: 978-3-319-26327-4
  • Book chapter
    Ahmadzadeh SR, Kormushev P, 2016,

    Visuospatial Skill Learning

    , Handling Uncertainty and Networked Structure in Robot Control, Editors: Busoniu, Tamás, Publisher: Springer International Publishing, Pages: 75-99, ISBN: 978-3-319-26327-4
  • Conference paper
    Maurelli F, Lane D, Kormushev P, Caldwell D, Carreras M, Salvi J, Fox M, Long D, Kyriakopoulos K, Karras Get al., 2016,

    The PANDORA project: a success story in AUV autonomy

    , OCEANS Conference 2016, Publisher: IEEE, ISSN: 0197-7385

    This paper presents some of the results of the EU-funded project PANDORA - Persistent Autonomy Through Learning Adaptation Observation and Re-planning. The project was three and a half years long and involved several organisations across Europe. The application domain is underwater inspection and intervention, a topic particularly interesting for the oil and gas sector, whose representatives constituted the Industrial Advisory Board. Field trials were performed at The Underwater Centre, in Loch Linnhe, Scotland, and in harbour conditions close to Girona, Spain.

  • Conference paper
    Kryczka P, Kormushev P, Tsagarakis N, Caldwell DGet al., 2015,

    Online Regeneration of Bipedal Walking Gait Optimizing Footstep Placement and Timing

  • Conference paper
    Kormushev P, Demiris Y, Caldwell DG, 2015,

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

  • Journal article
    Carrera A, Palomeras N, Hurtós N, Kormushev P, Carreras Met al., 2015,

    Cognitive System for Autonomous Underwater Intervention

    , Pattern Recognition Letters, ISSN: 0167-8655

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