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  • Conference paper
    Carrera A, Karras G, Bechlioulis C, Palomeras N, Hurtos N, Kyriakopoulos K, Kormushev P, Carreras Met al., 2014,

    Improving a Learning by Demonstration framework for Intervention AUVs by means of an UVMS controller

  • Conference paper
    Carrera A, Palomeras N, Ribas D, Kormushev P, Carreras Met al., 2014,

    An Intervention-AUV learns how to perform an underwater valve turning

  • Conference paper
    Jamali N, Kormushev P, Ahmadzadeh SR, Caldwell DGet al., 2014,

    Covariance Analysis as a Measure of Policy Robustness in Reinforcement Learning

  • Journal article
    Deisenroth MP, Fox D, Rasmussen CE, 2014,

    Gaussian Processes for Data-Efficient Learning in Robotics and Control

    , IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828

    Autonomous learning has been a promising direction in control and robotics for more than a decade since data-drivenlearning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcementlearning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in realsystems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learningapproaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, orspecific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extractingmore information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system.By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of modelerrors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves anunprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

  • Journal article
    Liepe J, Kirk P, Filippi S, Toni T, Barnes CP, Stumpf MPHet al., 2014,

    A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation

    , NATURE PROTOCOLS, Vol: 9, Pages: 439-456, ISSN: 1754-2189
  • Conference paper
    Carrera A, Palomeras N, Hurtos N, Kormushev P, Carreras Met al., 2014,

    Learning by demonstration applied to underwater intervention

  • Conference paper
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2013,

    Autonomous robotic valve turning: A hierarchical learning approach

    , 2013 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 4629-4634, ISSN: 1050-4729

    Autonomous valve turning is an extremely challenging task for an Autonomous Underwater Vehicle (AUV). To resolve this challenge, this paper proposes a set of different computational techniques integrated in a three-layer hierarchical scheme. Each layer realizes specific subtasks to improve the persistent autonomy of the system. In the first layer, the robot acquires the motor skills of approaching and grasping the valve by kinesthetic teaching. A Reactive Fuzzy Decision Maker (RFDM) is devised in the second layer which reacts to the relative movement between the valve and the AUV, and alters the robot's movement accordingly. Apprenticeship learning method, implemented in the third layer, performs tuning of the RFDM based on expert knowledge. Although the long-term goal is to perform the valve turning task on a real AUV, as a first step the proposed approach is tested in a laboratory environment. © 2013 IEEE.

  • Conference paper
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2013,

    Visuospatial Skill Learning for Object Reconfiguration Tasks

  • Conference paper
    Karras GC, Bechlioulis CP, Leonetti M, Palomeras N, Kormushev P, Kyriakopoulos KJ, Caldwell DGet al., 2013,

    On-Line Identification of Autonomous Underwater Vehicles through Global Derivative-Free Optimization

  • Conference paper
    Kormushev P, Caldwell DG, 2013,

    Improving the Energy Efficiency of Autonomous Underwater Vehicles by Learning to Model Disturbances

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