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  • Conference paper
    Dallali H, Kormushev P, Tsagarakis N, Caldwell DGet al., 2014,

    Can Active Impedance Protect Robots from Landing Impact?
  • Conference paper
    Ahmadzadeh SR, Jamisola RS, Kormushev P, Caldwell DGet al., 2014,

    Learning Reactive Robot Behavior for Autonomous Valve Turning
  • Conference paper
    Jamisola RS, Kormushev P, Bicchi A, Caldwell DGet al., 2014,

    Haptic Exploration of Unknown Surfaces with Discontinuities
  • Conference paper
    Jamali N, Kormushev P, Caldwell DG, 2014,

    Robot-Object Contact Perception using Symbolic Temporal Pattern Learning
  • Conference paper
    Ahmadzadeh SR, Carrera A, Leonetti M, Kormushev P, Caldwell DGet al., 2014,

    Online Discovery of AUV Control Policies to Overcome Thruster Failures
  • 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
    Jamali N, Kormushev P, Ahmadzadeh SR, Caldwell DGet al., 2014,

    Covariance Analysis as a Measure of Policy Robustness in Reinforcement Learning
  • 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
  • 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

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