The Experts below are selected from a list of 128619 Experts worldwide ranked by ideXlab platform

José Santos-victor - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Open and closed-loop task space trajectory control of redundant robots using Learned Models
    2013 IEEE RSJ International Conference on Intelligent Robots and Systems, 2013
    Co-Authors: Bruno Damas, Lorenzo Jamone, José Santos-victor
    Abstract:

    This paper presents a comparison of open-loop and closed-loop control strategies for tracking a task space trajectory, using redundant robots. We do not assume any knowledge of the analytical forward and inverse kinematics, relying instead on learning these Models online, while executing a desired task. Specifically, we employ a recent learning algorithm that allows to learn a probabilistic Model from which both the forward and inverse solutions can be obtained, as well as the Jacobian of the kinematics map. Such Learned Model can then be used to implement both types of control. Moreover, the multi-valued solutions provided by the Learned Model can be applied to redundant systems in which an infinite number of inverse solutions may exist. We present experiments with a simulated version of the iCub, a highly redundant humanoid robot, in which this Learned Model is employed to execute both open-loop and closed-loop trajectory control. We show the advantages and drawbacks of both control strategies, and we propose a way to combine them to deal with sensor noise and failures, showing the benefits of using a learning algorithm that can simultaneously provide forward and inverse predictions.

Jose M. F. Moura - One of the best experts on this subject based on the ideXlab platform.

  • GlobalSIP - SINGLE INDEX LATENT VARIABLE ModelS FOR NETWORK TOPOLOGY INFERENCE
    2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018
    Co-Authors: Jose M. F. Moura
    Abstract:

    A semi-parametric, non-linear regression Model in the presence of latent variables is applied towards learning network graph structure. These latent variables can correspond to un-Modeled phenomena or unmeasured agents in a complex system of interacting entities. This formulation jointly estimates non-linearities in the underlying data generation, the direct interactions between measured entities, and the indirect effects of unmeasured processes on the observed data. The learning is posed as regularized empirical risk minimization. Details of the algorithm for learning the Model are outlined. Experiments demonstrate the performance of the Learned Model on real data.

  • Single Index Latent Variable Models for Network Topology Inference
    arXiv: Machine Learning, 2018
    Co-Authors: Jose M. F. Moura
    Abstract:

    A semi-parametric, non-linear regression Model in the presence of latent variables is applied towards learning network graph structure. These latent variables can correspond to unModeled phenomena or unmeasured agents in a complex system of interacting entities. This formulation jointly estimates non-linearities in the underlying data generation, the direct interactions between measured entities, and the indirect effects of unmeasured processes on the observed data. The learning is posed as regularized empirical risk minimization. Details of the algorithm for learning the Model are outlined. Experiments demonstrate the performance of the Learned Model on real data.

Bruno Damas - One of the best experts on this subject based on the ideXlab platform.

  • open and closed loop task space trajectory control of redundant robots using Learned Models
    Intelligent Robots and Systems, 2013
    Co-Authors: Bruno Damas, Lorenzo Jamone, Jose Santosvictor
    Abstract:

    This paper presents a comparison of open-loop and closed-loop control strategies for tracking a task space trajectory, using redundant robots. We do not assume any knowledge of the analytical forward and inverse kinematics, relying instead on learning these Models online, while executing a desired task. Specifically, we employ a recent learning algorithm that allows to learn a probabilistic Model from which both the forward and inverse solutions can be obtained, as well as the Jacobian of the kinematics map. Such Learned Model can then be used to implement both types of control. Moreover, the multi-valued solutions provided by the Learned Model can be applied to redundant systems in which an infinite number of inverse solutions may exist. We present experiments with a simulated version of the iCub, a highly redundant humanoid robot, in which this Learned Model is employed to execute both open-loop and closed-loop trajectory control. We show the advantages and drawbacks of both control strategies, and we propose a way to combine them to deal with sensor noise and failures, showing the benefits of using a learning algorithm that can simultaneously provide forward and inverse predictions.

  • IROS - Open and closed-loop task space trajectory control of redundant robots using Learned Models
    2013 IEEE RSJ International Conference on Intelligent Robots and Systems, 2013
    Co-Authors: Bruno Damas, Lorenzo Jamone, José Santos-victor
    Abstract:

    This paper presents a comparison of open-loop and closed-loop control strategies for tracking a task space trajectory, using redundant robots. We do not assume any knowledge of the analytical forward and inverse kinematics, relying instead on learning these Models online, while executing a desired task. Specifically, we employ a recent learning algorithm that allows to learn a probabilistic Model from which both the forward and inverse solutions can be obtained, as well as the Jacobian of the kinematics map. Such Learned Model can then be used to implement both types of control. Moreover, the multi-valued solutions provided by the Learned Model can be applied to redundant systems in which an infinite number of inverse solutions may exist. We present experiments with a simulated version of the iCub, a highly redundant humanoid robot, in which this Learned Model is employed to execute both open-loop and closed-loop trajectory control. We show the advantages and drawbacks of both control strategies, and we propose a way to combine them to deal with sensor noise and failures, showing the benefits of using a learning algorithm that can simultaneously provide forward and inverse predictions.

Saddek Bensalem - One of the best experts on this subject based on the ideXlab platform.

  • Improved Learning for Stochastic Timed Models by State-Merging Algorithms
    2017
    Co-Authors: Braham Lotfi Mediouni, Ayoub Nouri, Marius Bozga, Saddek Bensalem
    Abstract:

    The construction of faithful system Models for quantitative analysis, e.g., performance evaluation, is challenging due to the inherent systems' complexity and unknown operating conditions. To overcome such difficulties, we are interested in the automated construction of system Models by learning from actual execution traces. We focus on the timing aspects of systems that are assumed to be of stochastic nature. In this context, we study a state-merging procedure for learning stochastic timed Models and we propose several enhancements at the level of the Learned Model structure and the underlying algorithms. The results obtained on different examples show a significant improvement of timing accuracy of the Learned Models.

  • NFM - Improved Learning for Stochastic Timed Models by State-Merging Algorithms
    Lecture Notes in Computer Science, 2017
    Co-Authors: Braham Lotfi Mediouni, Ayoub Nouri, Marius Bozga, Saddek Bensalem
    Abstract:

    The construction of faithful system Models for quantitative analysis, e.g., performance evaluation, is challenging due to the inherent systems’ complexity and unknown operating conditions. To overcome such difficulties, we are interested in the automated construction of system Models by learning from actual execution traces. We focus on the timing aspects of systems that are assumed to be of stochastic nature. In this context, we study a state-merging procedure for learning stochastic timed Models and we propose several enhancements at the level of the Learned Model structure and the underlying algorithms. The results obtained on different examples show a significant improvement of timing accuracy of the Learned Models.

Yunlong Liu - One of the best experts on this subject based on the ideXlab platform.

  • Basis selection in spectral learning of predictive state representations
    Neurocomputing, 2018
    Co-Authors: Chunqing Huang, Sun Zhou, Zhezheng Hong, Yunlong Liu
    Abstract:

    Abstract Predictive State Representations (PSRs) are powerful techniques for Modelling dynamical systems, which represent state as a vector of predictions about future observable events (tests). In PSRs, one of the fundamental problems is the learning of the PSR Model of the underlying system. Recently, spectral methods have been successfully used to address this issue by treating the learning problem as the task of computing Singular Value Decompositions (SVD)over a submatrix of a special type of matrix called the Hankel matrix. Under the assumptions that the rows and columns of the submatrix of the Hankel Matrix are sufficient (which usually means a very large number of rows and columns, and almost fails in practice) and the entries of the matrix can be estimated accurately, it has been proven that the spectral approach for learning PSRs is statistically consistent and the Learned parameters can converge to the true parameters. However, in practice, due to the limit of the computation ability, only a finite set of rows or columns can be chose to be used for the spectral learning. While different sets of columns usually lead to variant accuracy of the Learned Model, in this paper, we propose an approach for selecting the set of columns, namely basis selection, by adopting a concept of Model entropy to measure the accuracy of the Learned Model. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

  • Selecting Bases in Spectral learning of Predictive State Representations via Model Entropy
    arXiv: Learning, 2016
    Co-Authors: Yunlong Liu, Hexing Zhu
    Abstract:

    Predictive State Representations (PSRs) are powerful techniques for Modelling dynamical systems, which represent a state as a vector of predictions about future observable events (tests). In PSRs, one of the fundamental problems is the learning of the PSR Model of the underlying system. Recently, spectral methods have been successfully used to address this issue by treating the learning problem as the task of computing an singular value decomposition (SVD) over a submatrix of a special type of matrix called the Hankel matrix. Under the assumptions that the rows and columns of the submatrix of the Hankel Matrix are sufficient~(which usually means a very large number of rows and columns, and almost fails in practice) and the entries of the matrix can be estimated accurately, it has been proven that the spectral approach for learning PSRs is statistically consistent and the Learned parameters can converge to the true parameters. However, in practice, due to the limit of the computation ability, only a finite set of rows or columns can be chosen to be used for the spectral learning. While different sets of columns usually lead to variant accuracy of the Learned Model, in this paper, we propose an approach for selecting the set of columns, namely basis selection, by adopting a concept of Model entropy to measure the accuracy of the Learned Model. Experimental results are shown to demonstrate the effectiveness of the proposed approach.