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10th IFAC Symposium on Robot Control, SYROCO 2012

作者： Markdahl, Johan Karayiannidis, Yiannis Hu, Xiaoming Division of Optimization and Systems Theory KTH Royal Institute of Technology Stockholm Sweden Computer Vision and Active Perception Lab. KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9783902823113

This paper proposes a switched control algorithm for distributed cooperative manipulation of rigid bodies in a planar setting. More specifically, we consider the problem of making a grasped object follow a desired position and orientation path. The system contains N robots, possibly of heterogeneous designs, each of which consists of a manipulator arm and a nonholonomic mobile platform. Control is based on local information, is carried out on a kinematic level and partly utilizes inverse kinematics. We use Lyapunov-like arguments to prove that the algorithm is almost globally stable and also show that its parameters can be chosen so that any input saturation level is met. The time between certain switches is proved to be bounded below, and the system is shown to be free of any Zeno behavior. © 2012 IFAC.

关键词： Inverse kinematics

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Adaptive object centered teleoperation control of a mobile manipulator

Adaptive object centered teleoperation control of a mobile m...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Fredrik Båberg Yuquan Wang Sergio Caccamo Petter Ögren Computer Vision and Active Perception Lab Royal Institute of Technology (KTH)

Teleoperation of a mobile robot manipulating and exploring an object shares many similarities with the manipulation of virtual objects in a 3D design software such as AutoCAD. The user interfaces are however quite different, mainly for historical reasons. In this paper we aim to change that, and draw inspiration from the 3D design community to propose a teleoperation interface control mode that is identical to the ones being used to locally navigate the virtual viewpoint of most computer Aided Design (CAD) softwares. The proposed mobile manipulator control framework thus allows the user to focus on the 3D objects being manipulated, using control modes such as orbit object and pan object, supported by data from the wrist mounted RGB-D sensor. The gripper of the robot performs the desired motions relative to the object, while the manipulator arm and base moves in a way that realizes the desired gripper motions. The system redundancies are exploited in order to take additional constraints, such as obstacle avoidance, into account, using a constraint based programming framework.

关键词： Robot sensing systems Orbits End effectors Mobile communication Three-dimensional displays

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Learning grasp stability based on tactile data and HMMs

Learning grasp stability based on tactile data and HMMs

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19th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010

作者： Bekiroglu, Yasemin Kragic, Danica Kyrki, Ville Centre for Autonomous Systems and Computational Vision and Active Perception Lab. School of Computer Science and Communication KTH Stockholm Sweden Department of Information Technology Lappeenranta University of Technology Finland

ISBN: (纸本)9781424479917

In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability based on learning from examples. The main objective is classification of a grasp as stable or unstable before applying further actions on it, e.g. lifting. The problem cannot be solved by visual sensing which is typically used to execute an initial robot hand positioning with respect to the object. The output of the classification system can trigger a regrasping step if an unstable grasp is identified. An off-line learning process is implemented and used for reasoning about grasp stability for a three-fingered robotic hand using Hidden Markov models. To evaluate the proposed method, experiments are performed both in simulation and on a real robot system. © 2010 IEEE.

关键词： Hidden Markov models

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Learning task constraints for robot grasping using graphical models

Learning task constraints for robot grasping using graphical...

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23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010

作者： Song, D. Huebner, K. Kyrki, V. Kragic, D. KTH Royal Institute of Technology Stockholm Sweden Computer Vision and Active Perception Lab. Centre for Autonomous Systems Sweden Lappeenranta University of Technology Department of Information Technology Finland

ISBN: (纸本)9781424466757

This paper studies the learning of task constraints that allow grasp generation in a goal-directed manner. We show how an object representation and a grasp generated on it can be integrated with the task requirements. The scientific problems tackled are (i) identification and modeling of such task constraints, and (ii) integration between a semantically expressed goal of a task and quantitative constraint functions defined in the continuous object-action domains. We first define constraint functions given a set of object and action attributes, and then model the relationships between object, action, constraint features and the task using Bayesian networks. The probabilistic framework deals with uncertainty, combines apriori knowledge with observed data, and allows inference on target attributes given only partial observations. We present a system designed to structure data generation and constraint learning processes that is applicable to new tasks, embodiments and sensory data. The application of the task constraint model is demonstrated in a goal-directed imitation experiment. ©2010 IEEE.

关键词： Bayesian networks

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One Millisecond Face Alignment with an Ensemble of Regression Trees

One Millisecond Face Alignment with an Ensemble of Regressio...

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IEEE Conference on computer vision and Pattern Recognition

作者： Vahid Kazemi Josephine Sullivan KTH Royal Institute of Technology Computer Vision and Active Perception Lab

ISBN: (纸本)9781479951192

This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.

关键词： Shape Regression tree analysis Face Training Boosting Training data Vectors

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A Distributed Convergent Solution to the Ambulance Positioning Problem on a Streetmap Graph

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IFAC Proceedings Volumes 2014年第3期47卷 9190-9196页

作者： Yuquan Wang Michele Colledanchise Alejandro Marzinotto Petter Ögren The authors are with the computer vision and active perception lab royal institute of technology (kth) se-100 44 stockholm sweden

In this paper, we combine ideas from multi-agent cooperative coverage control, with problem formulations from the resource allocation field, to create a distributed convergent approach to the ambulance positioning problem. Inspired by coverage control we use the graph version of so-called Voronoi regions, making the solution distributed and reactive, thereby freeing computational resources. The solution is distributed in the sense that each vehicle only needs to know the positions of its neighbors, and the computations of each vehicle only depend on the size of its Voronoi region/set. This implies that considering a problem of twice the size, using twice the number of vehicles will leave the computational load per vehicle unchanged. The freed resources are used to capture the allocation problem in more detail: maximizing an estimate of the victim survival probability instead of more coarse measures of ambulance availability. Using real city street map data from OpenStreetMap (OSM), we provide simulation results illustrating the applicability of our approach. Finally, we prove that the proposed distributed algorithm is convergent in the sense that it finds a local optimum in finite time.

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Multi-scale conditional transition map: Modeling spatial-temporal dynamics of human movements with local and long-term correlations

Multi-scale conditional transition map: Modeling spatial-tem...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Zhan Wang P. Jensfelt J. Folkesson Computer Vision and Active Perception Lab The Royal Institute of Technology KTH Stockholm Sweden

This paper presents a novel approach to modeling the dynamics of human movements with a grid-based representation. The model we propose, termed as Multi-scale Conditional Transition Map (MCTMap), is an inhomogeneous HMM process that describes transitions of human location state in spatial and temporal space. Unlike existing work, our method is able to capture both local correlations and long-term dependencies on faraway initiating events. This enables the learned model to incorporate more information and to generate an informative representation of human existence probabilities across the grid map and along the temporal axis for intelligent interaction of the robot, such as avoiding or meeting the human. Our model consists of two levels. For each grid cell, we formulate the local dynamics using a variant of the left-to-right HMM, and thus explicitly model the exiting direction from the current cell. The dependency of this process on the entry direction is captured by employing the Input-Output HMM (IOHMM). On the higher level, we introduce the place where the whole trajectory originated into the IOHMM framework forming a hierarchical input structure to capture long-term dependencies. The capabilities of our method are verified by experimental results from 10 hours of data collected in an office corridor environment.

关键词： Hidden Markov models Correlation Trajectory Adaptation models Nonhomogeneous media Robots Heuristic algorithms

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SimTrack: A simulation-based framework for scalable real-time object pose detection and tracking

SimTrack: A simulation-based framework for scalable real-tim...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： K. Pauwels D. Kragic Computer Vision and Active Perception Lab KTH Royal Institute of Technology Stockholm Sweden

We propose a novel approach for real-time object pose detection and tracking that is highly scalable in terms of the number of objects tracked and the number of cameras observing the scene. Key to this scalability is a high degree of parallelism in the algorithms employed. The method maintains a single 3D simulated model of the scene consisting of multiple objects together with a robot operating on them. This allows for rapid synthesis of appearance, depth, and occlusion information from each camera viewpoint. This information is used both for updating the pose estimates and for extracting the low-level visual cues. The visual cues obtained from each camera are efficiently fused back into the single consistent scene representation using a constrained optimization method. The centralized scene representation, together with the reliability measures it enables, simplify the interaction between pose tracking and pose detection across multiple cameras. We demonstrate the robustness of our approach in a realistic manipulation scenario. We publicly release this work as a part of a general ROS software framework for real-time pose estimation, SimTrack, that can be integrated easily for different robotic applications.

关键词： Cameras Robot vision systems Visualization Robot kinematics Real-time systems

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Grasp moduli spaces and spherical harmonics

Grasp moduli spaces and spherical harmonics

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Florian T. Pokorny Yasemin Bekiroglu Danica Kragic Computer Vision and Active Perception Lab KTH Royal Institute of Technology Stockholm Sweden

In this work, we present a novel representation which enables a robot to reason about, transfer and optimize grasps on various objects by representing objects and grasps on them jointly in a common space. In our approach, objects are parametrized using smooth differentiable functions which are obtained from point cloud data via a spectral analysis. We show how, starting with point cloud data of various objects, one can utilize this space consisting of grasps and smooth surfaces in order to continuously deform various surface/grasp configurations with the goal of synthesizing force closed grasps on novel objects. We illustrate the resulting shape space for a collection of real world objects using multidimensional scaling and show that our formulation naturally enables us to use gradient ascent approaches to optimize and simultaneously deform a grasp from a known object towards a novel object.

关键词： Shape Three-dimensional displays Vectors Harmonic analysis Surface reconstruction Robot kinematics

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Learning Predictive State Representation for in-hand manipulation

Learning Predictive State Representation for in-hand manipul...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Johannes A. Stork Carl Henrik Ek Yasemin Bekiroglu Danica Kragic Computer Vision and Active Perception Lab KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781479969241

We study the use of Predictive State Representation (PSR) for modeling of an in-hand manipulation task through interaction with the environment. We extend the original PSR model to a new domain of in-hand manipulation and address the problem of partial observability by introducing new kernel-based features that integrate both actions and observations. The model is learned directly from haptic data and is used to plan series of actions that rotate the object in the hand to a specific configuration by pushing it against a table. Further, we analyze the model's belief states using additional visual data and enable planning of action sequences when the observations are ambiguous. We show that the learned representation is geometrically meaningful by embedding labeled action-observation traces. Suitability for planning is demonstrated by a post-grasp manipulation example that changes the object state to multiple specified target configurations.

关键词： Kernel History Robot sensing systems Planning Training Grippers

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