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

共 70 条记录，以下是31-40 订阅

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Probabilistic Object Tracking using a Range Camera

Probabilistic Object Tracking using a Range Camera

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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

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learning task error models for manipulation

Learning task error models for manipulation

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Peter Pastor Mrinal Kalakrishnan Jonathan Binney Jonathan Kelly Ludovic Righetti Gaurav Sukhatme Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Robotic Embedded Systems Laboratory University of Southern California Los Angeles CA USA University of Southern California Los Angeles CA US

Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipulation tasks, especially when visual servoing is rendered infeasible due to occlusions. A lot of research has been conducted to estimate geometric and non-geometric parameters of kinematic chains to minimize reconstruction errors. However, kinematic chains can include non-linearities, e.g. due to cable stretch and motor-side encoders, that result in significantly different errors for different parts of the state space. Previous work either does not consider such non-linearities or proposes to estimate non-geometric parameters of carefully engineered models that are robot specific. We propose a data-driven approach that learns task error models that account for such unmodeled non-linearities. We argue that in the context of grasping and manipulation, it is sufficient to achieve high accuracy in the task relevant state space. We identify this relevant state space using previously executed joint configurations and learn error corrections for those. Therefore, our system is developed to generate subsequent executions that are similar to previous ones. The experiments show that our method successfully captures the non-linearities in the head kinematic chain (due to a counterbalancing spring) and the arm kinematic chains (due to cable stretch) of the considered experimental platform, see Fig. 1. The feasibility of the presented error learning approach has also been evaluated in independent DARPA ARM-S testing contributing to successfully complete 67 out of 72 grasping and manipulation tasks.

关键词： Kinematics Joints Cameras Robots Training computational modeling Grasping

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Hypothesis testing framework for active object detection

Hypothesis testing framework for active object detection

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Nikolay Atanasov Bharath Sankaran Jerome Le Ny Thomas Koletschka George J. Pappas Kostas Daniilidis Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Department of Electrical Engineering Ecole Polytechnique de Montreal QUE Canada Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781467356404

One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of viewpoints, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active M-ary hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and experiments with real scenes captured by a kinect sensor. The results suggest a significant improvement over static object detection.

关键词： Vocabulary Feature extraction Mobile communication Object detection Robot sensing systems Planning Optimization

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Towards Associative Skill Memories

Towards Associative Skill Memories

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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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Quadratic programming for inverse dynamics with optimal distribution of contact forces

Quadratic programming for inverse dynamics with optimal dist...

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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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Movement segmentation and recognition for imitation learning 15

Movement segmentation and recognition for imitation learning

引用

15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012

作者： Meier, Franziska Theodorou, Evangelos Schaal, Stefan Computational Learning and Motor Control Lab University of Southern California Los Angeles United States Department of Computer Science and Engineering University of Washington Seattle United States Max-Planck-Institute for Intelligent Systems Tübingen Germany

In human movement learning, it is most common to teach constituent elements of complex movements in isolation, before chaining them into complex movements. Segmentation and recognition of observed movement could thus proceed out of this existing knowledge, which is directly compatible with movement generation. In this paper, we address exactly this scenario. We assume that a library of movement primitives has already been taught, and we wish to identify elements of the library in a complex motor act, where the individual elements have been smoothed together, and, occasionally, there might be a movement segment that is not in our library yet. We employ a flexible machine learning representation of movement primitives based on learnable nonlinear attractor system. For the purpose of movement segmentation and recognition, it is possible to reformulate this representation as a controlled linear dynamical system. An Expectation-Maximization algorithm can be developed to estimate the open parameters of a movement primitive from the library, using as input an observed trajectory piece. If no matching primitive from the library can be found, a new primitive is created. This process allows a straightforward sequential segmentation of observed movement into known and new primitives, which are suitable for robot imitation learning. We illustrate our approach with synthetic examples and data collected from human movement.

关键词： Maximum principle

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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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Encoding of periodic and their transient motions by a single dynamic movement primitive

Encoding of periodic and their transient motions by a single...

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IEEE-RAS International Conference on Humanoid Robots

作者： Johannes Ernesti Ludovic Righetti Martin Do Tamim Asfour Stefan Schaal Karlsruher Institut fur Technologie Karlsruhe Baden-WÃ¼rttemberg DE Computational Learning and Motor Control Lab University of Southern California Los Angeles California Institute for Anthropomatics Karlsruhe Institute of Technology Germany

Present formulations of periodic dynamic movement primitives (DMPs) do not encode the transient behavior required to start the rhythmic motion, although these transient movements are an important part of the rhythmic movements (i.e. when walking, there is always a first step that is very different from the subsequent ones). An ad-hoc procedure is then necessary to get the robot into the periodic motion. In this contribution we present a novel representation for rhythmic Dynamic Movement Primitives (DMPs) that encodes both the rhythmic motion and its transient behaviors. As with previously proposed DMPs, we use a dynamical system approach where an asymptotically stable limit cycle represents the periodic pattern. Transients are then represented as trajectories converging towards the limit cycle, different trajectories representing varying transients from different initial conditions. Our approach thus constitutes a generalization of previously proposed rhythmic DMPs. Experiments conducted on the humanoid robot ARMAR-III demonstrate the applicability of the approach for movement generation.

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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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