检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

58 篇 会议
12 篇 期刊文献

馆藏范围

70 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

37 篇 工学
- 20 篇 控制科学与工程
- 15 篇 机械工程
- 12 篇 仪器科学与技术
- 11 篇 计算机科学与技术...
- 11 篇 软件工程
- 5 篇 力学（可授工学、理...
- 3 篇 电气工程
- 2 篇 生物医学工程（可授...
- 2 篇 生物工程
- 1 篇 动力工程及工程热...
- 1 篇 电子科学与技术（可...
20 篇 理学
- 10 篇 系统科学
- 9 篇 数学
- 7 篇 物理学
- 3 篇 生物学
- 3 篇 统计学（可授理学、...
- 1 篇 地质学
3 篇 管理学
- 2 篇 管理科学与工程(可...
- 2 篇 工商管理
- 1 篇 图书情报与档案管...
2 篇 经济学
- 2 篇 应用经济学
1 篇 法学
- 1 篇 社会学
1 篇 医学
- 1 篇 临床医学

主题

14 篇 trajectory
11 篇 robots
8 篇 humanoid robots
7 篇 dynamics
6 篇 legged locomotio...
6 篇 force
6 篇 computational mo...
6 篇 joints
5 篇 noise
5 篇 learning
5 篇 robustness
4 篇 anthropomorphic ...
4 篇 optimal control
4 篇 torque
4 篇 shape
4 篇 cost function
3 篇 tuning
3 篇 grasping
3 篇 reinforcement le...
3 篇 force control

机构

14 篇 computational le...
7 篇 autonomous motio...
6 篇 computational le...
6 篇 computational le...
6 篇 computational le...
5 篇 computational le...
5 篇 max planck insti...
4 篇 bosch center for...
4 篇 computational le...
4 篇 computational le...
3 篇 university of so...
2 篇 tandon school of...
2 篇 computational le...
2 篇 department of ad...
2 篇 computational le...
2 篇 autonomous motio...
2 篇 disney research ...
2 篇 computational le...
2 篇 interdisciplinar...
2 篇 autonomous motio...

作者

38 篇 stefan schaal
20 篇 schaal stefan
15 篇 ludovic righetti
9 篇 peter pastor
8 篇 evangelos theodo...
8 篇 freek stulp
8 篇 jonas buchli
7 篇 mrinal kalakrish...
6 篇 nicholas rotella
6 篇 righetti ludovic
6 篇 trimpe sebastian
6 篇 jeannette bohg
5 篇 marco alonso
4 篇 franziska meier
4 篇 theodorou evange...
4 篇 michael mistry
4 篇 sean mason
4 篇 stulp freek
4 篇 buchli jonas
3 篇 sebastian trimpe

语言

69 篇 英文
1 篇 中文

检索条件"机构=Computational Learning and Motor Control"

共 70 条记录，以下是21-30 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Model-based policy search for automatic tuning of multivariate PID controllers

Model-based policy search for automatic tuning of multivaria...

引用

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

ISBN: (纸本)9781509046331

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 PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely based on data observed on an otherwise unknown system. The system's state is extended appropriately to frame the PID policy as a static state feedback policy. This renders PID tuning possible as the solution of a finite horizon optimal control problem without further a priori knowledge. The framework is applied to the task of balancing an inverted pendulum on a seven degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast and data-efficient policy learning, even on complex real world problems. © 2017 IEEE.

关键词： Inverted pendulum

来源：评论

学校读者我要写书评

暂无评论

Reinforcement learning of motor skills in high dimensions: A path integral approach

Reinforcement learning of motor skills in high dimensions: A...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Evangelos Theodorou Jonas Buchli Stefan Schaal Computational Learning and Motor Control Laboratory in Computer Science Biomedical Engineering Neuroscience University of Southern California USA

Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far due to the computational difficulties that reinforcement learning encounters in high dimensional continuous state-action spaces. In this paper, we derive a novel approach to RL for parameterized control policies based on the framework of stochastic optimal control with path integrals. While solidly grounded in optimal control theory and estimation theory, the update equations for learning are surprisingly simple and have no danger of numerical instabilities as neither matrix inversions nor gradient learning rates are required. Empirical evaluations demonstrate significant performance improvements over gradient-based policy learning and scalability to high-dimensional control problems. Finally, a learning experiment on a robot dog illustrates the functionality of our algorithm in a real-world scenario. We believe that our new algorithm, Policy Improvement with Path Integrals (PI 2 ), offers currently one of the most efficient, numerically robust, and easy to implement algorithms for RL in robotics.

关键词： Optimal control Function approximation Stochastic processes Integral equations Scalability Robots control systems learning systems Inference algorithms Stochastic resonance

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark

Evaluation of Policy Gradient Methods and Variants on the Ca...

引用

IEEE Symposium on Adaptive Dynamic Programming and Reinforcement learning, (ADPRL)

作者： Martin Riedmiller Jan Peters Stefan Schaal NeuroInformatics Group University of Osnabrück Germany Computational Learning and Motor Control University of Southern California USA

In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, 'vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease

关键词： Gradient methods learning Finite difference methods Solids Legged locomotion Stochastic processes Dynamic programming motor drives Optimization methods Regulators

来源：评论

学校读者我要写书评

暂无评论

Stochastic differential dynamic programming

Stochastic differential dynamic programming

引用

作者： Theodorou, Evangelos Tassa, Yuval Todorov, Emo Departments of Computer Science and Neuroscience Computational Learning and Motor Control Lab. University of Southern California United States Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Department of Computer Science and Engineering University of Washington Seattle WA United States

ISBN: (纸本)9781424474264

Although there has been a significant amount of work in the area of stochastic optimal control theory towards the development of new algorithms, the problem of how to control a stochastic nonlinear system remains an open research topic. Recent iterative linear quadratic optimal control methods iLQG [1], [2] handle control and state multiplicative noise while they are derived based on first order approximation of dynamics. On the other hand, methods such as Differential Dynamic Programming expand the dynamics up to the second order but so far they can handle nonlinear systems with additive noise. In this work we present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state and control multiplicative process noise, and proceed to derive the second-order expansion of the cost-to-go. We find the correction terms that arise from the stochastic assumption. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP. © 2010 AACC.

关键词： Additive noise

来源：评论

学校读者我要写书评

暂无评论

Fast, robust quadruped locomotion over challenging terrain

Fast, robust quadruped locomotion over challenging terrain

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Mrinal Kalakrishnan Jonas Buchli Peter Pastor Michael Mistry Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Disney Research Pittsburgh PA USA

We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization and control techniques to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal foothold choices from expert demonstration using terrain templates, (2) a body trajectory optimizer based on the Zero-Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller by testing it on the LittleDog quadruped robot, over a wide variety of rough terrain of varying difficulty levels. We demonstrate the generalization ability of this controller by presenting test results from an independent external test team on terrains that have never been shown to us.

关键词： Robustness Legged locomotion Force control Foot Leg Size control Robust control Mobile robots Software testing Optimal control

来源：评论

学校读者我要写书评

暂无评论

Probabilistic depth image registration incorporating nonvisual information

Probabilistic depth image registration incorporating nonvisu...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Manuel Wüthrich Peter Pastor Ludovic Righetti Aude Billard Stefan Schaal Faculty of Micro Engineering EPF Lausanne Switzerland Computational Learning and Motor Control Laboratory (CLMC) USC USA Learning Algorithms and Systems Laboratory (LASA) EPF Lausanne Switzerland

In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL [1] implementations of feature mapping and ICP, especially if nonvisual information is available.

关键词： Iterative closest point algorithm Cameras Robots Shape Covariance matrix Visualization Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Contact learning for Humanoid Estimation and control

Unsupervised Contact Learning for Humanoid Estimation and Co...

引用

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 sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.

关键词： Friction Sensors Force Foot State estimation computational modeling

来源：评论

学校读者我要写书评

暂无评论

On the Design of LQR Kernels for Efficient controller learning

On the Design of LQR Kernels for Efficient Controller Learni...

引用

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 trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.

关键词： Kernel Bayes methods Optimization Standards Adaptive control Nonlinear dynamical systems Optimal control

来源：评论

学校读者我要写书评

暂无评论

Trajectory formation for imitation with nonlinear dynamical systems

Trajectory formation for imitation with nonlinear dynamical ...

引用

IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： A.J. Ijspeert J. Nakanishi S. Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Kawato Dynamic Brain Project ATR Kyoto Japan

Explores an approach to learning by imitation and trajectory formation by representing movements as mixtures of nonlinear differential equations with well-defined attractor dynamics. An observed movement is approximated by finding a best fit of the mixture model to its data by a recursive least squares regression technique. In contrast to non-autonomous movement representations like splines, the resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy which is robust to strong external perturbations and that can be modified by additional perceptual variables. This movement policy remains the same for a given target, regardless of the initial conditions, and can easily be re-used for new targets. We evaluate the trajectory formation system in the context of a humanoid robot simulation that is part of the Virtual Trainer project, which aims at supervising rehabilitation exercises in stroke-patients. A typical rehabilitation exercise was collected with a Sarcos Sensuit, a device to record joint angular movement from human subjects, and approximated and reproduced with our imitation techniques. Our results demonstrate that multijoint human movements can be encoded successfully, and that this system allows robust modifications of the,movement policy through external variables.

关键词： Nonlinear dynamical systems control systems Least squares approximation Humanoid robots Robustness Spline Neural networks motor drives Laboratories Trajectory

来源：评论

学校读者我要写书评

暂无评论

Movement imitation with nonlinear dynamical systems in humanoid robots

Movement imitation with nonlinear dynamical systems in human...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： A.J. Ijspeert J. Nakanishi S. Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA ATR Human Information Systems Laboratories Kyoto Japan

Presents an approach to movement planning, on-line trajectory modification, and imitation learning by representing movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics. The resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy (CP) which is robust to strong external perturbations and that can be modified on-line by additional perceptual variables. We evaluate the system with a humanoid robot simulation and an actual humanoid robot. Experiments are presented for the imitation of three types of movements: reaching movements with one arm, drawing movements of 2-D patterns, and tennis swings. Our results demonstrate (a) that multi-joint human movements can be encoded successfully by the CPs, (b) that a learned movement policy can readily be reused to produce robust trajectories towards different targets, (c) that a policy fitted for one particular target provides a good predictor of human reaching movements towards neighboring targets, and (d) that the parameter space which encodes a policy is suitable for measuring to which extent two trajectories are qualitatively similar.

关键词： Nonlinear dynamical systems Humanoid robots Humans Robustness Laboratories Convergence Trajectory Encoding control systems Biological system modeling

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共7页 << < 1 2 3 4 5 6 7 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：