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检索条件"机构=Computational Learning and Motor Control lab"
48 条 记 录,以下是1-10 订阅
排序:
learning and Adaptation of Inverse Dynamics Models: A Comparison
Learning and Adaptation of Inverse Dynamics Models: A Compar...
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IEEE-RAS International Conference on Humanoid Robots
作者: Kevin Hitzler Franziska Meier Stefan Schaal Tamim Asfour Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Max-Planck-Institute of Intelligent Systems Germany and Computational Learning and Motor Control Lab University of Southern California USA
Performing tasks with high accuracy while interacting with the real world requires a robot to have an exact representation of its inverse dynamics that can be adapted to new situations. In the past, various methods fo... 详细信息
来源: 评论
Unsupervised Contact learning for Humanoid Estimation and control
Unsupervised Contact Learning for Humanoid Estimation and Co...
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IEEE International Conference on Robotics and Automation (ICRA)
作者: Nicholas Rotella Stefan Schaal Ludovic Righetti Computational Learning and Motor Control Lab University of Southern California Los Angeles California New York University New York New York
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
来源: 评论
An MPC Walking Framework With External Contact Forces
An MPC Walking Framework With External Contact Forces
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IEEE International Conference on Robotics and Automation
作者: Sean Mason Nicholas Rotella Stefan Schaal Ludovic Righetti Computational Learning and Motor Control Lab University of Southern California Los Angeles California Tandon School of Engineering New York University New York USA
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
来源: 评论
Probabilistic Recurrent State-Space Models
arXiv
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arXiv 2018年
作者: Doerr, Andreas Daniel, Christian Schiegg, Martin Nguyen-Tuong, Duy Schaal, Stefan Toussaint, Marc Trimpe, Sebastian Bosch Center for Artificial Intelligence Renningen Germany Max Planck Institute for Intelligent Systems Stuttgart/Tübingen Germany Computational Learning and Motor Control Lab University of Southern California United States Machine Learning and Robotics Lab University of Stuttgart Germany
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modelin... 详细信息
来源: 评论
Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization
Virtual vs. real: Trading off simulations and physical exper...
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2017 IEEE International Conference on Robotics and Automation, ICRA 2017
作者: Marco, Alonso Berkenkamp, Felix Hennig, Philipp Schoellig, Angela P. Krause, Andreas Schaal, Stefan Trimpe, Sebastian Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zurich Switzerland Canada Computational Learning and Motor Control Lab University of Southern California United States Max Planck ETH Center for Learning Systems Tubingen Germany Zurich Switzerland
In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to automate this process, yet often require... 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
learning task-specific dynamics to improve whole-body control
arXiv
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arXiv 2018年
作者: Gams, Andrej Mason, Sean A. Ude, Aleš Schaal, Stefan Righetti, Ludovic Humanoid and Cognitive Robotics Lab Dept. of Automatics Bio-cybernetics and Robotics Jožef Stefan Institute Ljubljana Slovenia Computational Learning and Motor Control Lab University of Southern California Los AngelesCA United States Tandon School of Engineering New York University New York United States Max Planck Institute for Intelligent Systems Tuebingen Germany
In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caus... 详细信息
来源: 评论
On the Design of LQR Kernels for Efficient controller learning
On the Design of LQR Kernels for Efficient Controller Learni...
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IEEE Annual Conference on Decision and control
作者: Alonso Marco Philipp Hennig Stefan Schaal Sebastian Trimpe Max Planck Institute for Intelligent Systems Tübingen Germany Computational Learning and Motor Control Lab University of Southern California Los Angeles USA
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trial... 详细信息
来源: 评论
On the design of LQR kernels for efficient controller learning
arXiv
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arXiv 2017年
作者: Marco, Alonso Hennig, Philipp Schaal, Stefan Trimpe, Sebastian Max Planck Institute for Intelligent Systems Tübingen72076 Germany Computational Learning and Motor Control Lab University of Southern California Los Angeles United States
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trial... 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
来源: 评论