检索结果-内蒙古大学图书馆

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 this end, we set up a two-step optimization problem. In the first optimization, we compute the Center of Mass (CoM) trajectory, foot step locations, and introduce slack variables to account for violating the imposed constraints on the Zero Moment Point (ZMP). We then use the slack variables to trigger the second optimization, in which we calculate the optimal external force that compensates for the ZMP tracking error. This optimization considers multiple contacts positions within the environment by formulating the problem as a Mixed Integer Quadratic Program (MIQP) that can be solved at a speed between 100-300 Hz. Once contact is created, the MIQP reduces to a single Quadratic Program (QP) that can be solved in real-time (< 1kHz). Simulations show that the presented walking control scheme can withstand disturbances 2-3× larger with the additional force provided by a hand contact.

关键词： Optimization Force Legged locomotion Trajectory Friction Dynamics

来源：评论

学校读者我要写书评

暂无评论

control of legged robots with optimal distribution of contact forces

Control of legged robots with optimal distribution of contac...

引用

2011 11th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS 2011

作者： Righetti, Ludovic Buchli, Jonas Mistry, Michael Schaal, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA 90089 United States Max Planck Institute for Intelligent Systems Tübingen Germany Dept. of Advanced Robotics Italian Institute of Technology Genoa Italy Disney Research Pittsburgh Pittsburgh PA 15213 United States

ISBN: (纸本)9781612848679

The development of agile and safe humanoid robots require controllers that guarantee both high tracking performance and compliance with the environment. More specifically, the control of contact interaction is of crucial importance for robots that will actively interact with their environment. Model-based controllers such as inverse dynamics or operational space control are very appealing as they offer both high tracking performance and compliance. However, while widely used for fully actuated systems such as manipulators, they are not yet standard controllers for legged robots such as humanoids. Indeed such robots are fundamentally different from manipulators as they are underactuated due to their floating-base and subject to switching contact constraints. In this paper we present an inverse dynamics controller for legged robots that use torque redundancy to create an optimal distribution of contact constraints. The resulting controller is able to minimize, given a desired motion, any quadratic cost of the contact constraints at each instant of time. In particular we show how this can be used to minimize tangential forces during locomotion, therefore significantly improving the locomotion of legged robots on difficult terrains. In addition to the theoretical result, we present simulations of a humanoid and a quadruped robot, as well as experiments on a real quadruped robot that demonstrate the advantages of the controller. © 2011 IEEE.

关键词： controllers

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

arXiv

引用

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 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. Copyright © 2017, The Authors. All rights reserved.

关键词： controllers

来源：评论

学校读者我要写书评

暂无评论

Efficient Bayesian Local Model learning for control

Efficient Bayesian Local Model Learning for Control

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Franziska Meier Philipp Hennig Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Angeles CA 90089 USA Max-Planck-Institute for Intelligent Systems 72076 Tubingen Germany

ISBN: (纸本)9781479969357

Model-based control is essential for compliant control and force control in many modern complex robots, like humanoid or disaster robots. Due to many unknown and hard to model nonlinearities, analytical models of such robots are often only very rough approximations. However, modern optimization controllers frequently depend on reasonably accurate models, and degrade greatly in robustness and performance if model errors are too large. For a long time, machine learning has been expected to provide automatic empirical model synthesis, yet so far, research has only generated feasibility studies but no learning algorithms that run reliably on complex robots. In this paper, we combine two promising worlds of regression techniques to generate a more powerful regression learning system. On the one hand, locally weighted regression techniques are computationally efficient, but hard to tune due to a variety of data dependent meta-parameters. On the other hand, Bayesian regression has rather automatic and robust methods to set learning parameters, but becomes quickly computationally infeasible for big and high-dimensional data sets. By reducing the complexity of Bayesian regression in the spirit of local model learning through variational approximations, we arrive at a novel algorithm that is computationally efficient and easy to initialize for robust learning. Evaluations on several datasets demonstrate very good learning performance and the potential for a general regression learning tool for robotics.

关键词： Robot compliance control Machine time Bayesian inference learning Humanoid Dataset model error learning algorithms Standing Orders Force control AUTOMATIC tuning robotics

来源：评论

学校读者我要写书评

暂无评论

Inertial Sensor-Based Humanoid Joint State Estimation

Inertial Sensor-Based Humanoid Joint State Estimation

引用

IEEE International Conference on Robotics and Automation

作者： Nicholas Rotella Sean Mason Stefan Schaal Ludovic Righetti Computational Learning and Motor Control Lab University of Southern California Los Angeles California Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

ISBN: (纸本)9781467380270

This work presents methods for the determination of a humanoid robot's joint velocities and accelerations directly from link-mounted Inertial Measurement Units (IMUs) each containing a three-axis gyroscope and a three-axis accelerometer. No information about the global pose of the floating base or its links is required and precise knowledge of the link IMU poses is not necessary due to presented calibration routines. Additionally, a filter is introduced to fuse gyroscope angular velocities with joint position measurements and compensate the computed joint velocities for time-varying gyroscope biases. The resulting joint velocities are subject to less noise and delay than filtered velocities computed from numerical differentiation of joint potentiometer signals, leading to superior performance in joint feedback control as demonstrated in experiments performed on a SARCOS hydraulic humanoid.

关键词： demonstrated experiments performed SARCOS

来源：评论

学校读者我要写书评

暂无评论

Balancing and walking using full dynamics LQR control with contact constraints

Balancing and walking using full dynamics LQR control with c...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Sean Mason Nicholas Rotella Stefan Schaal Ludovic Righetti Computational Learning and Motor Control Lab University of Southern California Los Angeles California Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

ISBN: (纸本)9781509047192

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 complexity. However, it is not clear how much complexity is really required to create controllers which exhibit good performance. In this paper, we study the capabilities of a simple approach based on contact consistent LQR controllers designed around key poses to control various tasks on a humanoid robot. We present extensive experimental results on a hydraulic, torque controlled humanoid performing balancing and stepping tasks. This feedback control approach captures the necessary synergies between the DoFs of the robot to guarantee good control performance. We show that for the considered tasks, it is only necessary to re-linearize the dynamics of the robot at different contact configurations and that increasing the number of LQR controllers along desired trajectories does not improve performance. Our result suggest that very simple controllers can yield good performance competitive with current state of the art, but more complex, optimization-based whole-body controllers. A video of the experiments can be found at https://***/5T08CNKV1hw.

关键词： Legged locomotion Dynamics Trajectory Robot kinematics Couplings Humanoid robots

来源：评论

学校读者我要写书评

暂无评论

Full dynamics LQR control of a humanoid robot: An experimental study on balancing and squatting

Full dynamics LQR control of a humanoid robot: An experiment...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Sean Mason Ludovic Righetti Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Anleles California Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

Humanoid robots operating in human environments require whole-body controllers that can offer precise tracking and well-defined disturbance rejection behavior. In this contribution, we propose an experimental evaluation of a linear quadratic regulator (LQR) using a linearization of the full robot dynamics together with the contact constraints. The advantage of the controller is that it explicitly takes into account the coupling between the different joints to create optimal feedback controllers for whole-body control. We also propose a method to explicitly regulate other tasks of interest, such as the regulation of the center of mass of the robot or its angular momentum. In order to evaluate the performance of linear optimal control designs in a real-world scenario (model uncertainty, sensor noise, imperfect state estimation, etc), we test the controllers in a variety of tracking and balancing experiments on a torque controlled humanoid (e.g. balancing, split plane balancing, squatting, pushes while squatting, and balancing on a wheeled platform). The proposed control framework shows a reliable push recovery behavior competitive with more sophisticated balance controllers, rejecting impulses up to 11.7 Ns with peak forces of 650 N, with the added advantage of great computational simplicity. Furthermore, the controller is able to track squatting trajectories up to 1 Hz without relinearization, suggesting that the linearized dynamics is sufficient for significant ranges of motion.

关键词： Joints Humanoid robots Iron Dynamics Aerospace electronics Couplings

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Object Tracking using a Range Camera

Probabilistic Object Tracking using a Range Camera

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Manuel Wuthrich Peter Pastor Mrinal Kalakrishnan Jeannette Bohg Stefan Schaal Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems Tubingen Germany Computational Learning and Motor Control lab at the University of Southern California Los Angeles CA USA

ISBN: (纸本)9781467363563

We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compute a posterior distribution over the current object pose. Depending on whether a robot or a human manipulates the object, we employ a process model with or without knowledge of control inputs. Observations are obtained from a range camera. As opposed to previous object tracking methods, we explicitly model self-occlusions and occlusions from the environment, e.g, the human or robotic hand. This leads to a strongly non-linear observation model and additional dependencies in the Bayesian network. We employ a Rao-Blackwellised particle filter to compute an estimate of the object pose at every time step. In a set of experiments, we demonstrate the ability of our method to accurately and robustly track the object pose in real-time while it is being manipulated by a human or a robot.

关键词： Probabilistic Object Tracking Range Camera 6-DoF

来源：评论

学校读者我要写书评

暂无评论

learning and Adaptation of Inverse Dynamics Models: A Comparison

Learning and Adaptation of Inverse Dynamics Models: A Compar...

引用

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

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

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 for learning inverse dynamics models have been proposed that combine the well-known rigid body dynamics with model-based parameter estimation, or learn directly on measured data using regression. However, there are still open questions regarding the efficiency of model-based learning compared to data-driven approaches as well as their capabilities to adapt to changing dynamics. In this paper, we compare the state-of-the-art inertial parameter estimation to a purely data-driven and a model-based approach on simulated and real data, collected with the humanoid robot Apollo. We further compare the adaptation capabilities of two models in a pick and place scenario while a) learning the model incrementally and b) extending the initially learned model with an error model. Based on this, we show the gap between simulation and reality and verify the importance of modeling nonlinear effects using regression. Furthermore, we demonstrate that error models outperform incremental learning regarding adaptation of inverse dynamics models.

关键词： Adaptation models Robots Mathematical model Inverse problems Dynamics Data models Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Balancing and walking using full dynamics LQR control with contact constraints

arXiv

引用

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

关键词： controllers

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：