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

共 48 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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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 to Grasp under Uncertainty

Learning to Grasp under Uncertainty

引用

2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Freek Stulp Evangelos Theodorou Jonas Buchli Stefan Schaal Computational Learning and Motor Control Lab University of Southern CaliforniaLos AngelesCA 90089

We present an approach that enables robots to learn motion primitives that are robust towards state estimation uncertainties. During reaching and preshaping, the robot learns to use fine manipulation strategies to maneuver the object into a pose at which closing the hand to perform the grasp is more likely to succeed. In contrast, common assumptions in grasp planning and motion planning for reaching are that these tasks can be performed independently, and that the robot has perfect knowledge of the pose of the objects in the environment. We implement our approach using Dynamic Movement Primitives and the probabilistic model-free reinforcement learning algorithm Policy Improvement with Path Integrals (PI2 ). The cost function that PI2 optimizes is a simple boolean that penalizes failed grasps. The key to acquiring robust motion primitives is to sample the actual pose of the object from a distribution that represents the state estimation uncertainty. During learning, the robot will thus optimize the chance of grasping an object from this distribution, rather than at one specific pose. In our empirical evaluation, we demonstrate how the motion primitives become more robust when grasping simple cylindrical objects, as well as more complex, non-convex objects. We also investigate how well the learned motion primitives generalize towards new object positions and other state estimation uncertainty distributions.

关键词： Uncertainty Trajectory Robots State estimation Grasping Robustness Planning

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Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization

Virtual vs. real: Trading off simulations and physical exper...

引用

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

ISBN: (纸本)9781509046331

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 requires too many experiments to be practical. In this paper, we propose a solution to this problem by exploiting prior knowledge from simulations, which are readily available for most robotic platforms. Specifically, we extend Entropy Search, a Bayesian optimization algorithm that maximizes information gain from each experiment, to the case of multiple information sources. The result is a principled way to automatically combine cheap, but inaccurate information from simulations with expensive and accurate physical experiments in a cost-effective manner. We apply the resulting method to a cart-pole system, which confirms that the algorithm can find good control policies with fewer experiments than standard Bayesian optimization on the physical system only. © 2017 IEEE.

关键词： Reinforcement learning

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

Compact models of motor primitive variations for predictable...

引用

9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09

作者： Stulp, Freek Oztop, Erhan Pastor, Peter Beetz, Michael Schaaz, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA United States Kyoto Japan Computational Neuroscience Laboratories Advanced Telecommunications Research Institute International Kyoto Japan Intelligent Autonomous Systems Group Technische Universitdt Munchen Munich Germany

ISBN: (纸本)9781424445882

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. ©2009 IEEE.

关键词： Anthropomorphic robots

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