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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

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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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learning force control policies for compliant manipulation

Learning force control policies for compliant manipulation

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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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Hierarchical reinforcement learning with movement primitives

Hierarchical reinforcement learning with movement primitives

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IEEE-RAS International Conference on Humanoid Robots

作者： Freek Stulp Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA

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, temporal abstractions are studied within the paradigm of hierarchical reinforcement learning. We propose a hierarchical reinforcement learning approach by applying our algorithm PI 2 to sequences of Dynamic Movement Primitives. For robots, this representation has some important advantages over discrete representations in terms of scalability and convergence speed. The parameters of the Dynamic Movement Primitives are learned simultaneously at different levels of temporal abstraction. The shape of a movement primitive is optimized w.r.t. the costs up to the next primitive in the sequence, and the subgoals between two movement primitives w.r.t. the costs up to the end of the entire movement primitive sequence. We implement our approach on an 11-DOF arm and hand, and evaluate it in a pick-and-place task in which the robot transports an object between different shelves in a cupboard.

关键词： Shape Trajectory learning Robots Aerospace electronics Noise Cost function

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Compliant quadruped locomotion over rough terrain

Compliant quadruped locomotion over rough terrain

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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

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learning motion primitive goals for robust manipulation

Learning motion primitive goals for robust manipulation

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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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

Applying model-free reinforcement learning to manipulation remains challenging for several reasons. First, manipulation involves physical contact, which causes discontinuous cost functions. Second, in manipulation, the end-point of the movement must be chosen carefully, as it represents a grasp which must be adapted to the pose and shape of the object. Finally, there is uncertainty in the object pose, and even the most carefully planned movement may fail if the object is not at the expected position. To address these challenges we 1) present a simplified, computationally more efficient version of our model-free reinforcement learning algorithm PI 2 ; 2) extend PI 2 so that it simultaneously learns shape parameters and goal parameters of motion primitives; 3) use shape and goal learning to acquire motion primitives that are robust to object pose uncertainty. We evaluate these contributions on a manipulation platform consisting of a 7-DOF arm with a 4-DOF hand.

关键词： Shape Trajectory Uncertainty Robots Cost function learning Grasping

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Movement segmentation using a primitive library

Movement segmentation using a primitive library

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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Franziska Meier Evangelos Theodorou Freek Stulp Stefan Schaal Computational Learning and Motor Control laboratory University of Southern California Los Angeles CA USA

Segmenting complex movements into a sequence of primitives remains a difficult problem with many applications in the robotics and vision communities. In this work, we show how the movement segmentation problem can be reduced to a sequential movement recognition problem. To this end, we reformulate the original Dynamic Movement Primitive (DMP) formulation as a linear dynamical system with control inputs. Based on this new formulation, we develop an Expectation-Maximization algorithm to estimate the duration and goal position of a partially observed trajectory. With the help of this algorithm and the assumption that a library of movement primitives is present, we present a movement segmentation framework. We illustrate the usefulness of the new DMP formulation on the two applications of online movement recognition and movement segmentation.

关键词： Trajectory Libraries Motion segmentation Covariance matrix Noise Equations

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Editorial

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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

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

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

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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

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Probabilistic depth image registration incorporating nonvisual information

Probabilistic depth image registration incorporating nonvisu...

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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

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