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检索条件"机构=Computational Learning for Autonomous Systems"
28 条 记 录,以下是1-10 订阅
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Recurrent Kaiman networks: Factorized inference in high-dimensional deep feature spaces  36
Recurrent Kaiman networks: Factorized inference in high-dime...
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36th International Conference on Machine learning, ICML 2019
作者: Becker, Philipp Pandya, Harit Gebhardt, Gregor Zhao, Cheng Taylor, James Neumann, Gerhard Computational Learning for Autonomous Systems TU Darmstadt Darmstadt Germany Bosch Center for Artificial Intelligence Renningen Germany University of Tübingen Tübingen Germany Lincoln Center for Autonomous Systems University of Lincoln Lincoln United Kingdom Extreme Robotics Lab University of Birmingham Birmingham United Kingdom Engineering Department Lancaster University Lancaster United Kingdom
In order to integrate uncertainty estimates into deep time-series modelling, Kaiman Filters (KFs) (Kaiman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approx... 详细信息
来源: 评论
Recurrent kalman networks: Factorized inference in high-dimensional deep feature spaces
arXiv
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arXiv 2019年
作者: Becker, Philipp Pandya, Harit Gebhardt, Gregor Zhao, Cheng Taylor, James Neumann, Gerhard Computational Learning for Autonomous Systems TU Darmstadt Darmstadt Germany Bosch Center for Artificial Intelligence Renningen Germany University of Tübingen Tübingen Germany Lincoln Center for Autonomous Systems University of Lincoln Lincoln United Kingdom Extreme Robotics Lab University of Birmingham Birmingham United Kingdom Engineering Department Lancaster University Lancaster United Kingdom
In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approx... 详细信息
来源: 评论
Model-based policy search for automatic tuning of multivariate PID controllers
Model-based policy search for automatic tuning of multivaria...
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2017 IEEE International Conference on Robotics and Automation, ICRA 2017
作者: Doerr, Andreas Nguyen-Tuong, Duy Marco, Alonso Schaal, Stefan Trimpe, Sebastian Bosch Center for Artificial Intelligence Renningen Germany Autonomous Motion Department Max Planck Institute for Intelligent Systems Tübingen Germany Computational Learning and Motor Control Lab University of Southern California Los AngelesCA United States
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILC... 详细信息
来源: 评论
Balancing and walking using full dynamics LQR control with contact constraints
arXiv
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arXiv 2017年
作者: Mason, Sean Rotella, Nicholas Schaal, Stefan Righetti, Ludovic Computational Learning and Motor Control Lab University of Southern California Los AngelesCA United States Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany
Torque control algorithms which consider robot dynamics and contact constraints are important for creating dynamic behaviors for humanoids. As computational power increases, algorithms tend to also increase in complex... 详细信息
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Combining learned and analytical models for predicting action effects from sensory data
arXiv
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arXiv 2017年
作者: Kloss, Alina Schaal, Stefan Bohg, Jeannette Autonomous Motion Department Max Planck Institute for Intelligent Systems Germany Computational Learning and Motor Control Lab University of Southern California United States Department of Computer Science Stanford University United States
One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally... 详细信息
来源: 评论
Model-based policy search for automatic tuning of multivariate PID controllers
Model-based policy search for automatic tuning of multivaria...
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IEEE International Conference on Robotics and Automation (ICRA)
作者: Andreas Doerr Duy Nguyen-Tuong Alonso Marco Stefan Schaal Sebastian Trimpe Autonomous Motion Department at the Max Planck Institute for Intelligent Systems Tübingen Germany Bosch Center for Artificial Intelligence Renningen Germany Computational Learning and Motor Control lab at the University of Southern California Los Angeles CA USA
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILC... 详细信息
来源: 评论
Model-based policy search for automatic tuning of multivariate pid controllers
arXiv
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arXiv 2017年
作者: Doerr, Andreas Nguyen-Tuong, Duy Marco, Alonso Schaal, Stefan Trimpe, Sebastian Bosch Center for Artificial Intelligence Renningen Germany Autonomous Motion Department Max Planck Institute for Intelligent Systems Tübingen Germany Computational Learning and Motor Control lab University of Southern California Los AngelesCA United States
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILC... 详细信息
来源: 评论
Unsupervised Contact learning for Humanoid Estimation and Control
arXiv
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arXiv 2017年
作者: Rotella, Nicholas Schaal, Stefan Righetti, Ludovic Computational Learning and Motor Control Lab University of Southern California Los AngelesCA United States Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany New York University New YorkNY United States
This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive se... 详细信息
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An MPC walking framework with external contact forces
arXiv
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arXiv 2017年
作者: Mason, Sean Rotella, Nicholas Schaal, Stefan Righetti, Ludovic Computational Learning and Motor Control Lab. University of Southern California Los AngelesCA United States Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany Tandon School of Engineering New York University New York United States
In this work, we present an extension to a linear Model Predictive Control (MPC) scheme that plans external contact forces for the robot when given multiple contact locations and their corresponding friction cone. To ... 详细信息
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Optimal control and inverse optimal control by distribution matching
Optimal control and inverse optimal control by distribution ...
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IEEE/RSJ International Conference on Intelligent Robots and systems
作者: Oleg Arenz Hany Abdulsamad Gerhard Neumann Computational Learning for Autonomous Systems TU Darmstadt 64287 Germany Intelligent Autonomous Systems TU Darmstadt 64287 Germany
Optimal control is a powerful approach to achieve optimal behavior. However, it typically requires a manual specification of a cost function which often contains several objectives, such as reaching goal positions at ... 详细信息
来源: 评论