Ara es mostren els items 14-18 de 18

  • Reinforcement learning for robot control using probability density estimations 

    Agostini, Alejandro Gabriel; Celaya Llover, Enric (INSTICC Press. Institute for Systems and Technologies of Information, Control and Communication, 2010)
    Text en actes de congrés
    Accés restringit per política de l'editorial
    The successful application of Reinforcement Learning (RL) techniques to robot control is limited by the fact that, in most robotic tasks, the state and action spaces are continuous, multidimensional, and in essence, too ...
  • Reinforcement learning with a Gaussian mixture model 

    Agostini, Alejandro Gabriel; Celaya Llover, Enric (2010)
    Text en actes de congrés
    Accés obert
    Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q Iteration and the use of Gaussian Processes. They belong to the class of fitted value iteration algorithms, which use a ...
  • Robot task specification and execution through relational positioning 

    Rodríguez Tsouroukdissian, Adolfo; Basañez Villaluenga, Luis; Celaya Llover, Enric (IFAC, 2007)
    Text en actes de congrés
    Accés obert
    This paper presents a relational positioning methodology for flexibly and intuitively specifying offline programmed robot tasks, and for assisting the execution of teleoperated tasks featuring precise or repetitive movements. ...
  • Stochastic approximations of average values using proportions of samples 

    Agostini, Alejandro Gabriel; Celaya Llover, Enric (2011)
    Report de recerca
    Accés obert
    In this work we explain how the stochastic approximation of the average of a random variable is carried out when the observations used in the updates consist in proportion of samples rather than complete samples.
  • Visually-guided robot navigation: From artificial to natural landmarks 

    Celaya Llover, Enric; Albarral, José L.; Jimenez Schlegl, Pablo; Torras, Carme (Springer, 2007)
    Capítol de llibre
    Accés obert
    Landmark-based navigation in unknown unstructured environments is far from solved. The bottleneck nowadays seems to be the fast detection of reliable visual references in the image stream as the robot moves. In our research, ...