检索结果-内蒙古大学图书馆

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

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

Compliant quadruped locomotion over rough terrain

Compliant quadruped locomotion over rough terrain

引用

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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

Many critical elements for statically stable walking for legged robots have been known for a long time, including stability criteria based on support polygons, good foothold selection, recovery strategies to name a few. All these criteria have to be accounted for in the planning as well as the control phase. Most legged robots usually employ high gain position control, which means that it is crucially important that the planned reference trajectories are a good match for the actual terrain, and that tracking is accurate. Such an approach leads to conservative controllers, i.e. relatively low speed, ground speed matching, etc. Not surprisingly such controllers are not very robust - they are not suited for the real world use outside of the laboratory where the knowledge of the world is limited and error prone. Thus, to achieve robust robotic locomotion in the archetypical domain of legged systems, namely complex rough terrain, where the size of the obstacles are in the order of leg length, additional elements are required. A possible solution to improve the robustness of legged locomotion is to maximize the compliance of the controller. While compliance is trivially achieved by reduced feedback gains, for terrain requiring precise foot placement (e.g. climbing rocks, walking over pegs or cracks) compliance cannot be introduced at the cost of inferior tracking. Thus, model-based control and - in contrast to passive dynamic walkers - active balance control is required. To achieve these objectives, in this paper we add two crucial elements to legged locomotion, i.e., floating-base inverse dynamics control and predictive force control, and we show that these elements increase robustness in face of unknown and unanticipated perturbations (e.g. obstacles). Furthermore, we introduce a novel line-based COG trajectory planner, which yields a simpler algorithm than traditional polygon based methods and creates the appropriate input to our control *** show results from bot

关键词： Legged locomotion Force control Robust control Robustness Stability criteria Position control Trajectory Laboratories Error correction Leg

来源：评论

学校读者我要写书评

暂无评论

Compact models of motor primitive variations for predictable reaching and obstacle avoidance

Compact models of motor primitive variations for predictable...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Freek Stulp Erhan Oztop Peter Pastor Michael Beetz Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Intelligent Autonomous Systems Group Technische Universität München Munich Germany Dokuritsu Gyosei Hojin Joho Tsushin Kenkyu Kiko Koganei Tokyo JP University of Southern California Los Angeles CA US

ISBN: (纸本)9781424445875

In most activities of daily living, related tasks are encountered over and over again. This regularity allows humans and robots to reuse existing solutions for known recurring tasks. We expect that reusing a set of standard solutions to solve similar tasks will facilitate the design and on-line adaptation of the control systems of robots operating in human environments. In this paper, we derive a set of standard solutions for reaching behavior from human motion data. We also derive stereotypical reaching trajectories for variations of the task, in which obstacles are present. These stereotypical trajectories are then compactly represented with Dynamic Movement Primitives. On the humanoid robot Sarcos CB, this approach leads to reproducible, predictable, and human-like reaching motions.

关键词： Predictive models

来源：评论

学校读者我要写书评

暂无评论

Editorial

引用

International Journal of Humanoid Robotics 2005年第4期2卷 389-390页

作者： CHENG, GORDON SCHAAL, STEFAN ATKESON, CHRISTOPHER G. JST-ICORP Computational Brain Project ATR Computational Neuroscience Laboratory 2-2-2 Keihanna Science City Soraku-gun Kyoto619-0288 Japan Computational Learning and Motor Control Lab University of Southern California Hedco Neurosciences Building HNB-103 3641 Watt Way Los AngelesCA90089-2520 United States Robotics Institute Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States

来源：评论

学校读者我要写书评

暂无评论

learning movement primitives

Springer Tracts in Advanced Robotics

引用

Springer Tracts in Advanced Robotics 2005年 15卷 561-572页

作者： Schaal, Stefan Peters, Jan Nakanishi, Jun Ijspeert, Auke Computational Learning and Motor Control Laboratory Computer Science and Neuroscience University of Southern California Los Angeles CA 90089-2520 United States Dept of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratory 2-2-2 Hikaridai Seika-cho Soraku-gun 619-0288 Kyoto Japan School of Computer and Communication Sciences EPFL Swiss Federal Institute of Technology Lausanne CH 1015 Lausanne Switzerland

This paper discusses a comprehensive framework for modular motor control based on a recently developed theory of dynamic movement primitives (DMP). DMPs are a formulation of movement primitives with autonomous nonlinear differential equations, whose time evolution creates smooth kinematic control policies. Model-based control theory is used to convert the outputs of these policies into motor commands. By means of coupling terms, on-line modifications can be incorporated into the time evolution of the differential equations, thus providing a rather flexible and reactive framework for motor planning and execution. The linear parameterization of DMPs lends itself naturally to supervised learning from demonstration. Moreover, the temporal, scale, and translation invariance of the differential equations with respect to these parameters provides a useful means for movement recognition. A novel reinforcement learning technique based on natural stochastic policy gradients allows a general approach of improving DMPs by trial and error learning with respect to almost arbitrary optimization criteria. We demonstrate the different ingredients of the DMP approach in various examples, involving skill learning from demonstration on the humanoid robot DB, and learning biped walking from demonstration in simulation, including self-improvement of the movement patterns towards energy efficiency through resonance tuning. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Differential equations

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

Trajectory formation for imitation with nonlinear dynamical systems

Trajectory formation for imitation with nonlinear dynamical ...

引用

2001 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Ijspeert, Auke Jan Nakanishi, Jun Schaal, Stefan Computational Learning Motor Control Laboratory University of Southern California Los Angeles CA 90089-2520 United States

This article explores a new 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 (TFS) in the context of a humanoid robot simulation that is part of the Virtual Trainer (VT) 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： Anthropomorphic robots

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

A 3-D biomechanical model of the salamander 2

引用

2nd International Conference on Virtual Worlds, VW 2000

作者： Jan, Auke Brain Simulation Laboratory and Computational Learning and Motor Control Laboratory University of Southern California Hedco Neuroscience Building Los AngelesCA90089 United States

ISBN: (纸本)3540677070

This article describes a 3D biomechanical simulation of a salamander to be used in experiments in computational neuroethology. The physically-based simulation represents the salamander as an articulated body, actuated by muscles simulated as springs and dampers, in interaction with a simple environment. The aim of the simulation is to investigate the neural circuits underlying the aquatic and terrestrial locomotion of the real salamander, as well as to serve as test bed for investigating vertebrate sensorimotor coordination in silico. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Biomechanics

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：