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

共 70 条记录，以下是1-10 订阅

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Reinforcement learning of full-body humanoid motor skills

Reinforcement learning of full-body humanoid motor skills

引用

2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010

作者： Stulp, Freek Buchli, Jonas Theodorou, Evangelos Schaal, Stefan Computational Learning and Motor Control Lab University of Southern California Los Angeles CA 90089 United States

ISBN: (纸本)9781424486885

Applying reinforcement learning to humanoid robots is challenging because humanoids have a large number of degrees of freedom and state and action spaces are continuous. Thus, most reinforcement learning algorithms would become computationally infeasible and require a prohibitive amount of trials to explore such high-dimensional spaces. In this paper, we present a probabilistic reinforcement learning approach, which is derived from the framework of stochastic optimal control and path integrals. The algorithm, called Policy Improvement with Path Integrals (PI2), has a surprisingly simple form, has no open tuning parameters besides the exploration noise, is modelfree, and performs numerically robustly in high dimensional learning *** demonstrate how PI2 is able to learn full-body motor skills on a 34-DOF humanoid robot. To demonstrate the generality of our approach, we also apply PI2 in the context of variable impedance control, where both planned trajectories and gain schedules for each joint are optimized simultaneously. ©2010 IEEE.

关键词： Anthropomorphic robots

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Hierarchical reinforcement learning with movement primitives

Hierarchical reinforcement learning with movement primitives

引用

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

作者： Stulp, Freek Schaal, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA 90089 United States

Temporal abstraction and task decomposition drastically reduce the search space for planning and control, and are fundamental to making complex tasks amenable to learning. In the context of reinforcement learning, tem... 详细信息

ISBN: (纸本)9781612848679

关键词： Reinforcement learning

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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

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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

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An iterative path integral stochastic optimal control approach for learning robotic tasks

An iterative path integral stochastic optimal control approa...

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作者： Theodorou, Evangelos Stulp, Freek Buchli, Jonas Schaal, Stefan Computational Learning and Motor Control Lab. University of Southern California United States Department of Advanced Robotics Italian Institute of Technology Italy ATR Computational Neuroscience Laboratories Kyoto 619-0288 Japan

ISBN: (纸本)9783902661937

Recent work on path integral stochastic optimal control theory Theodorou et al. (2010a);Theodorou (2011) has shown promising results in planning and control of nonlinear systems in high dimensional state spaces. The path integral control framework relies on the transformation of the nonlinear Hamilton Jacobi Bellman (HJB) partial differential equation (PDE) into a linear PDE and the approximation of its solution via the use of the Feynman Kac lemma. In this work, we are reviewing the generalized version of path integral stochastic optimal control formalism Theodorou et al. (2010a), used for optimal control and planing of stochastic dynamical systems with state dependent control and diffusion matrices. Moreover we present the iterative path integral control approach, the so called Policy Improvement with Path Integrals or (PI 2) which is capable of scaling in high dimensional robotic control problems. Furthermore we present a convergence analysis of the proposed algorithm and we apply the proposed framework to a variety of robotic tasks. Finally with the goal to perform locomotion the iterative path integral control is applied for learning nonlinear limit cycle attractors with adjustable land scape. © 2011 IFAC.

关键词： Iterative methods

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Compliant control for quadrupedal walking over rough terrain 13th

Compliant control for quadrupedal walking over rough terrain

引用

13th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2010

作者： Buchli, Jonas Kalakrishnan, Mrinal Mistry, Michael Pastor, Peter Schaal, Stefan Computational Learning and Motor Control Lab University of Southern California Los AngelesCA90089 United States Disney Research PittsburghPA15213 United States

ISBN: (纸本)9789814327978

An often used stability criterion in legged locomotion is the zero moment point (ZMP). The ZMP is a virtual point calculated based on the center of gravity (COG) position and acceleration and must be kept within the support polygon at all times for the robot to be stable. Therefore, maintaining stability relies critically on good tracking of the planned COG trajectory and traditionally high gain PID control is the most commonly used to ensure good tracking performance. However, PID control has a severe disadvantage in that it relies critically on exact knowledge of the terrain and exhibits suboptimal stiff behavior in case of external perturbations. Here we will show that the inverse dynamics controller allows us to lower the PID gains while at the same time maintaining sufficient tracking performance. The main contributions of this paper are (a) the formulation of the ZMP planning as constrained optimization problem, and the proof of its performance on difficult terrain (b) inverse dynamics based compliant control to ensure tracking of the ZMP and (c) showing that the compliant controller outperforms high-gain controllers in case of severely degraded terrain perception and thus increases the robustness in locomotion over difficult terrain. We present results on the quadruped robot LittleDog. © 2010 by World Scientific Publishing Co. Pte. Ltd.

关键词： Three term control systems

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Towards Associative Skill Memories

Towards Associative Skill Memories

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Peter Pastor Mrinal Kalakrishnan Ludovic Righetti Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Angeles USA

Movement primitives as basis of movement planning and control have become a popular topic in recent years. The key idea of movement primitives is that a rather small set of stereotypical movements should suffice to create a large set of complex manipulation skills. An interesting side effect of stereotypical movement is that it also creates stereotypical sensory events, e.g., in terms of kinesthetic variables, haptic variables, or, if processed appropriately, visual variables. Thus, a movement primitive executed towards a particular object in the environment will associate a large number of sensory variables that are typical for this manipulation skill. These association can be used to increase robustness towards perturbations, and they also allow failure detection and switching towards other behaviors. We call such movement primitives augmented with sensory associations Associative Skill Memories (ASM). This paper addresses how ASMs can be acquired by imitation learning and how they can create robust manipulation skill by determining subsequent ASMs online to achieve a particular manipulation goal. Evaluation for grasping and manipulation with a Barrett WAM/Hand illustrate our approach.

关键词： Robot sensing systems Force Fingers Torque Trajectory Uncertainty

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Reinforcement learning of full-body humanoid motor skills

Reinforcement learning of full-body humanoid motor skills

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IEEE-RAS International Conference on Humanoid Robots

作者： Freek Stulp Jonas Buchli Evangelos Theodorou Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Angeles CA

Applying reinforcement learning to humanoid robots is challenging because humanoids have a large number of degrees of freedom and state and action spaces are continuous. Thus, most reinforcement learning algorithms would become computationally infeasible and require a prohibitive amount of trials to explore such high-dimensional spaces. In this paper, we present a probabilistic reinforcement learning approach, which is derived from the framework of stochastic optimal control and path integrals. The algorithm, called Policy Improvement with Path Integrals (PI 2 ), has a surprisingly simple form, has no open tuning parameters besides the exploration noise, is model-free, and performs numerically robustly in high dimensional learning problems. We demonstrate how PI 2 is able to learn full-body motor skills on a 34-DOF humanoid robot. To demonstrate the generality of our approach, we also apply PI 2 in the context of variable impedance control, where both planned trajectories and gain schedules for each joint are optimized simultaneously.

关键词： Trajectory Joints Optimal control learning Noise Humanoid robots

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Quadratic programming for inverse dynamics with optimal distribution of contact forces

Quadratic programming for inverse dynamics with optimal dist...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Ludovic Righetti Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Angeles USA

In this contribution we propose an inverse dynamics controller for a humanoid robot that exploits torque redundancy to minimize any combination of linear and quadratic costs in the contact forces and the commands. In addition the controller satisfies linear equality and inequality constraints in the contact forces and the commands such as torque limits, unilateral contacts or friction cones limits. The originality of our approach resides in the formulation of the problem as a quadratic program where we only need to solve for the control commands and where the contact forces are optimized implicitly. Furthermore, we do not need a structured representation of the dynamics of the robot (i.e. an explicit computation of the inertia matrix). It is in contrast with existing methods based on quadratic programs. The controller is then robust to uncertainty in the estimation of the dynamics model and the optimization is fast enough to be implemented in high bandwidth torque control loops that are increasingly available on humanoid platforms. We demonstrate properties of our controller with simulations of a human size humanoid robot.

关键词： Dynamics Robots Acceleration Force Equations Torque Foot

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learning force control policies for compliant manipulation

Learning force control policies for compliant manipulation

引用

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Mrinal Kalakrishnan Ludovic Righetti Peter Pastor Stefan Schaal Computational Learning and Motor Control laboratory University of Southern California Los Angeles CA USA

Developing robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manipulation tasks imply complex contact interactions with the external world, and involve reasoning about the forces and torques to be applied. Planning under contact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learning. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI 2 ) algorithm to learn these force/torque profiles by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.

关键词： Trajectory Force Robots Cost function Noise Torque Joints

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