Browsing by Author "Celaya Llover, Enric"
Now showing items 1-20 of 27
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A competitive strategy for function approximation in Q-learning
(2011)
Conference report
Open AccessIn this work we propose an approach for generalization in continuous domain Reinforcement Learning that, instead of using a single function approximator, tries many different function approximators in parallel, each one ... -
A Legendre-Gauss pseudospectral collocation method for trajectory optimization in second order systems
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Conference report
Open AccessPseudospectral collocation methods have proven to be powerful tools to solve optimal control problems. While these methods generally assume the dynamics is given in the first order form xdot = f(x, u, t), where x is the ... -
A modification of collocation methods to ensure compatible position and velocity trajectories. Documento técnico del Instituto de Robótica e Informática Industrial
(2021-05-26)
Research report
Open Access -
A relational positioning methodology for robot task specification and execution
(IEEE, 2008)
Article
Open AccessThis paper presents a relational positioning methodology that allows to restrict totally or partially the movements of an object by specifying its allowed positions in terms of a set of intuitive geometric constraints. In ... -
Action rule induction from cause-effect pairs learned through robot-teacher interaction
(University of Karlsruhe, 2008)
Conference report
Open AccessIn this work we propose a decision-making system that efficiently learns behaviors in the form of rules using natural human instructions about cause-effect relations in currently observed situations, avoiding complicated ... -
Action rule induction from cause-effect pairs learned through robot-teacher interaction
(2008)
Conference report
Open AccessIn this work we propose a decision-making system that efficiently learns behaviors in the form of rules using natural human instructions about cause-effect relations in currently observed situations, avoiding complicated ... -
Collocation methods for second order systems
(2022)
Conference report
Open AccessCollocation methods for numerical optimal control commonly assume that the system dynamics is expressed as a first order ODE of the form x¿ = f(x, u, t), where x is the state and u the control vector. However, in many ... -
Competitive function approximation for reinforcement learning
(2014)
Research report
Open AccessThe application of reinforcement learning to problems with continuous domains requires representing the value function by means of function approximation. We identify two aspects of reinforcement learning that make the ... -
Description of a robotics-oriented relational positioning methodology
(2007-09)
Research report
Open AccessThis paper presents a relational positioning methodology for flexibly and intuitively specifying offline programmed robot tasks, as well as for assisting the execution of teleoperated tasks demanding precise movements. In ... -
Exact interval propagation for the efficient solution of planar linkages
(ASME, 2007)
Conference report
Open AccessThis paper presents an interval propagation algorithm for variables in single-loop linkages. Given allowed intervals of values for all variables, the algorithm provides, for every variable, the exact interval of values for ... -
Exact interval propagation for the efficient solution of position analysis problems on planar linkages
(2012)
Article
Open AccessThis paper presents an interval propagation algorithm for variables in planar single-loop linkages. Given intervals of allowed values for all variables, the algorithm provides, for every variable, the whole set of values, ... -
Learning rules from cause-effects explanations
(2008)
Research report
Open AccessIn this work we propose a learning system to learn on-line an action policy coded in rules using natural human instructions about cause-effect relations in currently observed situations. The instructions only on currently ... -
Model predictive control for a Mecanum-wheeled robot navigating among obstacles
(Elsevier, 2021)
Conference report
Open AccessMecanum-wheeled robots have been thoroughly used to automate tasks in many different applications. However, they are usually controlled by neglecting their dynamics and relying only on their kinematic model. In this paper, ... -
Natural landmark detection for visually-guided robot navigation
(Springer, 2007)
Part of book or chapter of book
Open AccessThe main difficulty to attain fully autonomous robot navigation outdoors is the fast detection of reliable visual references, and their subsequent characterization as landmarks for immediate and unambiguous recognition. ... -
Natural landmark detection for visually-guided robot navigation
(2007)
Conference report
Open AccessThe main difficulty to attain fully autonomous robot navigation outdoors is the fast detection of reliable visual references, and their subsequent characterization as landmarks for immediate and unambiguous recognition. ... -
On-line learning of macro planning operators using probabilistic estimations of cause-effects
(2008)
Research report
Open AccessIn this work we propose an on-line learning method for learning action rules for planning. The system uses a probabilistic approach of a constructive induction method that combines a beam search with an example-based search ... -
Online EM with weight-based forgetting
(2015)
Article
Open AccessIn the on-line version of the EM algorithm introduced by Sato and Ishii (2000), a time-dependent discount factor is introduced for forgetting the effect of the old posterior values obtained with an earlier, inaccurate ... -
Online reinforcement learning using a probability density estimation
(The MIT Press. Massachusetts Institute of Technology, 2017-01-01)
Article
Open AccessFunction approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are ... -
Probability density estimation of the Q Function for reinforcement learning
(2009)
Research report
Open AccessPerforming Q-Learning in continuous state-action spaces is a problem still unsolved for many complex applications. The Q function may be rather complex and can not be expected to fit into a predefined parametric model. In ... -
Reinforcement learning for robot control using probability density estimations
(INSTICC Press. Institute for Systems and Technologies of Information, Control and Communication, 2010)
Conference report
Restricted access - publisher's policyThe 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 ...
