The Experts below are selected from a list of 1259994 Experts worldwide ranked by ideXlab platform
Jan Peters - One of the best experts on this subject based on the ideXlab platform.
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incremental online sparsification for Model Learning in real time robot control
Neurocomputing, 2011Co-Authors: Duy Nguyentuong, Jan PetersAbstract:For many applications such as compliant, accurate robot tracking control, dynamics Models learned from data can help to achieve both compliant control performance as well as high tracking quality. Online Learning of these dynamics Models allows the robot controller to adapt itself to changes in the dynamics (e.g., due to time-variant nonlinearities or unforeseen loads). However, online Learning in real-time applications - as required in control - cannot be realized by straightforward usage of off-the-shelf machine Learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for fast real-time Model Learning. The proposed approach employs a sparsification method based on an independence measure. In combination with an incremental Learning approach such as incremental Gaussian process regression, we obtain a Model approximation method which is applicable in real-time online Learning. It exhibits competitive Learning accuracy when compared with standard regression techniques. Implementation on a real Barrett WAM robot demonstrates the applicability of the approach in real-time online Model Learning for real world systems.
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Model Learning for robot control a survey
Cognitive Processing, 2011Co-Authors: Duy Nguyentuong, Jan PetersAbstract:Models are among the most essential tools in robotics, such as kinematics and dynamics Models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal Models in order to generate their actions. However, while classical robotics relies on manually generated Models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate Models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in Model Learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a Model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of Model-based Learning control, we view the Model from three different perspectives. First, we need to study the different possible Model Learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable Learning methods. From this discussion, we deduce future directions of real-time Learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.
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Model Learning with local gaussian process regression
Advanced Robotics, 2009Co-Authors: Duy Nguyentuong, Matthias Seeger, Jan PetersAbstract:Precise Models of robot inverse dynamics allow the design of significantly more accurate, energy-efficient and compliant robot control. However, in some cases the accuracy of rigid-body Models does not suffice for sound control performance due to unModeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse dynamics Model from measured data poses an interesting alternative. Nonparametric regression methods, such as Gaussian process regression (GPR) or locally weighted projection regression (LWPR), are not as restrictive as parametric Models and, thus, offer a more flexible framework for approximating unknown nonlinearities. In this paper, we propose a local approximation to the standard GPR, called local GPR (LGP), for real-time Model online Learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR. The approach is shown to have competitive Learning performance for...
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local gaussian process regression for real time online Model Learning and control
Neural Information Processing Systems, 2008Co-Authors: Duy Nguyentuong, Jan Peters, Matthias SeegerAbstract:Learning in real-time applications, e.g., online approximation of the inverse dynamics Model for Model-based robot control, requires fast online regression techniques. Inspired by local Learning, we propose a method to speed up standard Gaussian process regression (GPR) with local GP Models (LGP). The training data is partitioned in local regions, for each an individual GP Model is trained. The prediction for a query point is performed by weighted estimation using nearby local Models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online Learning and prediction in real-time. Comparisons with other non-parametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and ν-SVR.
Duy Nguyentuong - One of the best experts on this subject based on the ideXlab platform.
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incremental online sparsification for Model Learning in real time robot control
Neurocomputing, 2011Co-Authors: Duy Nguyentuong, Jan PetersAbstract:For many applications such as compliant, accurate robot tracking control, dynamics Models learned from data can help to achieve both compliant control performance as well as high tracking quality. Online Learning of these dynamics Models allows the robot controller to adapt itself to changes in the dynamics (e.g., due to time-variant nonlinearities or unforeseen loads). However, online Learning in real-time applications - as required in control - cannot be realized by straightforward usage of off-the-shelf machine Learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for fast real-time Model Learning. The proposed approach employs a sparsification method based on an independence measure. In combination with an incremental Learning approach such as incremental Gaussian process regression, we obtain a Model approximation method which is applicable in real-time online Learning. It exhibits competitive Learning accuracy when compared with standard regression techniques. Implementation on a real Barrett WAM robot demonstrates the applicability of the approach in real-time online Model Learning for real world systems.
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Model Learning for robot control a survey
Cognitive Processing, 2011Co-Authors: Duy Nguyentuong, Jan PetersAbstract:Models are among the most essential tools in robotics, such as kinematics and dynamics Models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal Models in order to generate their actions. However, while classical robotics relies on manually generated Models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate Models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in Model Learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a Model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of Model-based Learning control, we view the Model from three different perspectives. First, we need to study the different possible Model Learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable Learning methods. From this discussion, we deduce future directions of real-time Learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.
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Model Learning with local gaussian process regression
Advanced Robotics, 2009Co-Authors: Duy Nguyentuong, Matthias Seeger, Jan PetersAbstract:Precise Models of robot inverse dynamics allow the design of significantly more accurate, energy-efficient and compliant robot control. However, in some cases the accuracy of rigid-body Models does not suffice for sound control performance due to unModeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse dynamics Model from measured data poses an interesting alternative. Nonparametric regression methods, such as Gaussian process regression (GPR) or locally weighted projection regression (LWPR), are not as restrictive as parametric Models and, thus, offer a more flexible framework for approximating unknown nonlinearities. In this paper, we propose a local approximation to the standard GPR, called local GPR (LGP), for real-time Model online Learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR. The approach is shown to have competitive Learning performance for...
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local gaussian process regression for real time online Model Learning and control
Neural Information Processing Systems, 2008Co-Authors: Duy Nguyentuong, Jan Peters, Matthias SeegerAbstract:Learning in real-time applications, e.g., online approximation of the inverse dynamics Model for Model-based robot control, requires fast online regression techniques. Inspired by local Learning, we propose a method to speed up standard Gaussian process regression (GPR) with local GP Models (LGP). The training data is partitioned in local regions, for each an individual GP Model is trained. The prediction for a query point is performed by weighted estimation using nearby local Models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online Learning and prediction in real-time. Comparisons with other non-parametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and ν-SVR.
Min Wang - One of the best experts on this subject based on the ideXlab platform.
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deep multiscale Model Learning
Journal of Computational Physics, 2020Co-Authors: Yalchin Efendiev, Eric T Chung, Yating Wang, Siu Wun Cheung, Min WangAbstract:Abstract The objective of this paper is to design novel multi-layer neural networks for multiscale simulations of flows taking into account the observed fine data and physical Modeling concepts. Our approaches use deep Learning techniques combined with local multiscale Model reduction methodologies to predict flow dynamics. Using reduced-order Model concepts is important for constructing robust deep Learning architectures since the reduced-order Models provide fewer degrees of freedom. We consider flow dynamics in porous media as multi-layer networks in this work. More precisely, the solution (e.g., pressures and saturation) at the time instant n + 1 depends on the solution at the time instant n and input parameters, such as permeability fields, forcing terms, and initial conditions. One can regard the solution as a multi-layer network, where each layer, in general, is a nonlinear forward map and the number of layers relates to the internal time steps. We will rely on rigorous Model reduction concepts to define unknowns and connections between layers. It is critical to use reduced-order Models for this purpose, which will identify the regions of influence and the appropriate number of variables. Furthermore, due to the lack of available observed fine data, the reduced-order Model can provide us sufficient inexpensive data as needed. The designed deep neural network will be trained using both coarse simulation data which is obtained from the reduced-order Model and observed fine data. We will present the main ingredients of our approach and numerical examples. Numerical results show that using deep Learning with data generated from multiscale Models as well as available observed fine data, we can obtain an improved forward map which can better approximate the fine scale Model.
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deep multiscale Model Learning
arXiv: Numerical Analysis, 2018Co-Authors: Yalchin Efendiev, Eric T Chung, Yating Wang, Siu Wun Cheung, Min WangAbstract:The objective of this paper is to design novel multi-layer neural network architectures for multiscale simulations of flows taking into account the observed data and physical Modeling concepts. Our approaches use deep Learning concepts combined with local multiscale Model reduction methodologies to predict flow dynamics. Using reduced-order Model concepts is important for constructing robust deep Learning architectures since the reduced-order Models provide fewer degrees of freedom. Flow dynamics can be thought of as multi-layer networks. More precisely, the solution (e.g., pressures and saturations) at the time instant $n+1$ depends on the solution at the time instant $n$ and input parameters, such as permeability fields, forcing terms, and initial conditions. One can regard the solution as a multi-layer network, where each layer, in general, is a nonlinear forward map and the number of layers relates to the internal time steps. We will rely on rigorous Model reduction concepts to define unknowns and connections for each layer. In each layer, our reduced-order Models will provide a forward map, which will be modified ("trained") using available data. It is critical to use reduced-order Models for this purpose, which will identify the regions of influence and the appropriate number of variables. Because of the lack of available data, the training will be supplemented with computational data as needed and the interpolation between data-rich and data-deficient Models. We will also use deep Learning algorithms to train the elements of the reduced Model discrete system. We will present main ingredients of our approach and numerical results. Numerical results show that using deep Learning and multiscale Models, we can improve the forward Models, which are conditioned to the available data.
Yating Wang - One of the best experts on this subject based on the ideXlab platform.
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deep multiscale Model Learning
Journal of Computational Physics, 2020Co-Authors: Yalchin Efendiev, Eric T Chung, Yating Wang, Siu Wun Cheung, Min WangAbstract:Abstract The objective of this paper is to design novel multi-layer neural networks for multiscale simulations of flows taking into account the observed fine data and physical Modeling concepts. Our approaches use deep Learning techniques combined with local multiscale Model reduction methodologies to predict flow dynamics. Using reduced-order Model concepts is important for constructing robust deep Learning architectures since the reduced-order Models provide fewer degrees of freedom. We consider flow dynamics in porous media as multi-layer networks in this work. More precisely, the solution (e.g., pressures and saturation) at the time instant n + 1 depends on the solution at the time instant n and input parameters, such as permeability fields, forcing terms, and initial conditions. One can regard the solution as a multi-layer network, where each layer, in general, is a nonlinear forward map and the number of layers relates to the internal time steps. We will rely on rigorous Model reduction concepts to define unknowns and connections between layers. It is critical to use reduced-order Models for this purpose, which will identify the regions of influence and the appropriate number of variables. Furthermore, due to the lack of available observed fine data, the reduced-order Model can provide us sufficient inexpensive data as needed. The designed deep neural network will be trained using both coarse simulation data which is obtained from the reduced-order Model and observed fine data. We will present the main ingredients of our approach and numerical examples. Numerical results show that using deep Learning with data generated from multiscale Models as well as available observed fine data, we can obtain an improved forward map which can better approximate the fine scale Model.
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deep multiscale Model Learning
arXiv: Numerical Analysis, 2018Co-Authors: Yalchin Efendiev, Eric T Chung, Yating Wang, Siu Wun Cheung, Min WangAbstract:The objective of this paper is to design novel multi-layer neural network architectures for multiscale simulations of flows taking into account the observed data and physical Modeling concepts. Our approaches use deep Learning concepts combined with local multiscale Model reduction methodologies to predict flow dynamics. Using reduced-order Model concepts is important for constructing robust deep Learning architectures since the reduced-order Models provide fewer degrees of freedom. Flow dynamics can be thought of as multi-layer networks. More precisely, the solution (e.g., pressures and saturations) at the time instant $n+1$ depends on the solution at the time instant $n$ and input parameters, such as permeability fields, forcing terms, and initial conditions. One can regard the solution as a multi-layer network, where each layer, in general, is a nonlinear forward map and the number of layers relates to the internal time steps. We will rely on rigorous Model reduction concepts to define unknowns and connections for each layer. In each layer, our reduced-order Models will provide a forward map, which will be modified ("trained") using available data. It is critical to use reduced-order Models for this purpose, which will identify the regions of influence and the appropriate number of variables. Because of the lack of available data, the training will be supplemented with computational data as needed and the interpolation between data-rich and data-deficient Models. We will also use deep Learning algorithms to train the elements of the reduced Model discrete system. We will present main ingredients of our approach and numerical results. Numerical results show that using deep Learning and multiscale Models, we can improve the forward Models, which are conditioned to the available data.
Matthias Seeger - One of the best experts on this subject based on the ideXlab platform.
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Model Learning with local gaussian process regression
Advanced Robotics, 2009Co-Authors: Duy Nguyentuong, Matthias Seeger, Jan PetersAbstract:Precise Models of robot inverse dynamics allow the design of significantly more accurate, energy-efficient and compliant robot control. However, in some cases the accuracy of rigid-body Models does not suffice for sound control performance due to unModeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse dynamics Model from measured data poses an interesting alternative. Nonparametric regression methods, such as Gaussian process regression (GPR) or locally weighted projection regression (LWPR), are not as restrictive as parametric Models and, thus, offer a more flexible framework for approximating unknown nonlinearities. In this paper, we propose a local approximation to the standard GPR, called local GPR (LGP), for real-time Model online Learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR. The approach is shown to have competitive Learning performance for...
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local gaussian process regression for real time online Model Learning and control
Neural Information Processing Systems, 2008Co-Authors: Duy Nguyentuong, Jan Peters, Matthias SeegerAbstract:Learning in real-time applications, e.g., online approximation of the inverse dynamics Model for Model-based robot control, requires fast online regression techniques. Inspired by local Learning, we propose a method to speed up standard Gaussian process regression (GPR) with local GP Models (LGP). The training data is partitioned in local regions, for each an individual GP Model is trained. The prediction for a query point is performed by weighted estimation using nearby local Models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online Learning and prediction in real-time. Comparisons with other non-parametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and ν-SVR.
