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International Conference on Advanced Robotics (ICAR)

作者： Jeannette Bohg Danica Kragic Centre of Autonomous Systems Computer Vision and Active Perception Laboratory Stockholm Sweden

We present work on vision based robotic grasping. The proposed method relies on extracting and representing the global contour of an object in a monocular image. A suitable grasp is then generated using a learning framework where prototypical grasping points are learned from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labeled synthetic images. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects. Furthermore, we will show how our representation supports the inference of a full grasp configuration.

关键词： Humans Robot kinematics Robot vision systems Supervised learning Object detection Cameras Shape control computer vision laboratories Prototypes

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Learning Predictive State Representations for planning

Learning Predictive State Representations for planning

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： J. A. Stork C. H. Ek D. Kragic Computer Vision and Active Perception Lab Centre for Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden Computer Vision and Active Perception Lab Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

Predictive State Representations (PSRs) allow modeling of dynamical systems directly in observables and without relying on latent variable representations. A problem that arises from learning PSRs is that it is often hard to attribute semantic meaning to the learned representation. This makes generalization and planning in PSRs challenging. In this paper, we extend PSRs and introduce the notion of PSRs that include prior information (P-PSRs) to learn representations which are suitable for planning and interpretation. By learning a low-dimensional embedding of test features we map belief points of similar semantic to the same region of a subspace. This facilitates better generalization for planning and semantical interpretation of the learned representation. In specific, we show how to overcome the training sample bias and introduce feature selection such that the resulting representation emphasizes observables related to the planning task. We show that our P-PSRs result in qualitatively meaningful representations and present quantitative results that indicate improved suitability for planning.

关键词： Planning History Robots Semantics Predictive models Uncertainty

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In-hand manipulation using gravity and controlled slip

In-hand manipulation using gravity and controlled slip

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IEEE/RSJ International Conference on Intelligent Robots and systems, IROS 2015

作者： Vina B., Francisco E. Karayiannidis, Yiannis Pauwels, Karl Smith, Christian Kragic, Danica Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH StockholmSE-100 44 Sweden Dept. of Signals and Systems Chalmers University of Technology GothenburgSE-412 96 Sweden

ISBN: (纸本)9781479999941

In this work we propose a sliding mode controller for in-hand manipulation that repositions a tool in the robot's hand by using gravity and controlling the slippage of the tool. In our approach, the robot holds the tool with a pinch grasp and we model the system as a link attached to the gripper via a passive revolute joint with friction, i.e., the grasp only affords rotational motions of the tool around a given axis of rotation. The robot controls the slippage by varying the opening between the fingers in order to allow the tool to move to the desired angular position following a reference trajectory. We show experimentally how the proposed controller achieves convergence to the desired tool orientation under variations of the tool's inertial parameters. © 2015 IEEE.

关键词： Sliding mode control

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Adaptive control for pivoting with visual and tactile feedback

Adaptive control for pivoting with visual and tactile feedba...

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2016 IEEE International Conference on Robotics and Automation, ICRA 2016

作者： Vina, Francisco E.B. Karayiannidis, Yiannis Smith, Christian Kragic, Danica Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH StockholmSE-100 44 Sweden Dept. of Signals and Systems Chalmers University of Technology GothenburgSE-412 96 Sweden

ISBN: (纸本)9781467380263

In this work we present an adaptive control approach for pivoting, which is an in-hand manipulation maneuver that consists of rotating a grasped object to a desired orientation relative to the robot's hand. We perform pivoting by means of gravity, allowing the object to rotate between the fingers of a one degree of freedom gripper and controlling the gripping force to ensure that the object follows a reference trajectory and arrives at the desired angular position. We use a visual pose estimation system to track the pose of the object and force measurements from tactile sensors to control the gripping force. The adaptive controller employs an update law that accommodates for errors in the friction coefficient, which is one of the most common sources of uncertainty in manipulation. Our experiments confirm that the proposed adaptive controller successfully pivots a grasped object in the presence of uncertainty in the object's friction parameters. © 2016 IEEE.

关键词： Friction

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Integrating Grasp Planning with Online Stability Assessment using Tactile Sensing

Integrating Grasp Planning with Online Stability Assessment ...

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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： Yasemin Bekiroglu Kai Huebner Danica Kragic Computer Vision and Active Perception Lab Centre for Autonomous SystemsSchool of Computer Science and CommunicationKTHStockholmSweden

This paper presents an integration of grasp planning and online grasp stability assessment based on tactile data. We show how the uncertainty in grasp execution posterior to grasp planning can be dealt with using tactile sensing and machine learning techniques. The majority of the state-of-theart grasp planners demonstrate impressive results in simulation. However, these results are mostly based on perfect scene/object knowledge allowing for analytical measures to be employed. It is questionable how well these measures can be used in realistic scenarios where the information about the object and robot hand may be incomplete and/or uncertain. Thus, tactile and force-torque sensory information is necessary for successful online grasp stability assessment. We show how a grasp planner can be integrated with a probabilistic technique for grasp stability assessment in order to improve the hypotheses about suitable grasps on different types of objects. Experimental evaluation with a three-fingered robot hand equipped with tactile array sensors shows the feasibility and strength of the integrated approach.

关键词： Hidden Markov models Stability analysis Tactile sensors Grasping

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Adaptive control for pivoting with visual and tactile feedback

Adaptive control for pivoting with visual and tactile feedba...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Francisco E. Viña B. Yiannis Karayiannidis Christian Smith Danica Kragic Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH Stockholm Sweden Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Chalmers University of Technology Sweden Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH Sweden

关键词： Friction Force Grippers Robot kinematics Planning Robot sensing systems

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Classical Grasp Quality Evaluation: New Algorithms and Theory

Classical Grasp Quality Evaluation: New Algorithms and Theor...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Florian T. Pokorny Danica Kragic Computer Vision and Active Perception Lab Centre for Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

This paper investigates theoretical properties of a well-known L~1 grasp quality measure Q whose approximation Q_l~- is commonly used for the evaluation of grasps and where the precision of Q_l~- depends on an approximation of a cone by a convex polyhedral cone with l edges. We prove the Lipschitz continuity of Q and provide an explicit Lipschitz bound that can be used to infer the stability of grasps lying in a neighbourhood of a known grasp. We think of Q_l~- as a lower bound estimate to Q and describe an algorithm for computing an upper bound Q~+. We provide worst-case error bounds relating Q and Q_l~-. Furthermore, we develop a novel grasp hypothesis rejection algorithm which can exclude unstable grasps much faster than current implementations. Our algorithm is based on a formulation of the grasp quality evaluation problem as an optimization problem, and we show how our algorithm can be used to improve the efficiency of sampling based grasp hypotheses generation methods.

关键词： New Algorithms Theory evaluation of grasps Grasp theories Quality factor formula quality evaluation approximate formula algorithms Lower bound Neighborhood

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Adaptive Force/Velocity control for opening unknown doors 1

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IFAC Proceedings Volumes 2012年第22期45卷 753-758页

作者： Yiannis Karayiannidis Christian Smith Petter Ögren Danica Kragic Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology (KTH) SE-100 44 Stockholm Sweden

The problem of door opening is fundamental for robots operating in domestic environments. Since these environments are generally unstructured, a robot must deal with several types of uncertainties associated with the dynamics and kinematics of a door to achieve successful opening. The present paper proposes a dynamic force/velocity controller which uses adaptive estimation of the radial direction based on adaptive estimates of the door hinge's position. The control action is decomposed into estimated radial and tangential directions, which are proved to converge to the corresponding actual values. The force controller uses reactive compensation of the tangential forces and regulates the radial force to a desired small value, while the velocity controller ensures that the robot's end-effector moves with a desired tangential velocity. The performance of the control scheme is demonstrated in simulation with a 2 DoF planar manipulator opening a door.

关键词： door opening force/velocity control adaptive estimation

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Friction Coefficients and Grasp Synthesis

Friction Coefficients and Grasp Synthesis

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Kaiyu Hang Florian T. Pokorny Danica Kragic Computer Vision and Active Perception Lab Centre for Autonomous Systems CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

We propose a new concept called friction sensitivity which measures how susceptible a specific grasp is to changes in the underlying friction coefficients. We develop algorithms for the synthesis of stable grasps with low friction sensitivity and for the synthesis of stable grasps in the case of small friction coefficients. We describe how grasps with low friction sensitivity can be used when a robot has an uncertain belief about friction coefficients and study the statistics of grasp quality under changes in those coefficients. We also provide a parametric estimate for the distribution of grasp qualities and friction sensitivities for a uniformly sampled set of grasps.

关键词： Friction Coefficients Grasp Synthesis underlying friction coefficients Grasp coefficient of friction friction synthesis

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Interactive object classification using sensorimotor contingencies

Interactive object classification using sensorimotor conting...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Virgile Hogman Marten Bjorkman Danica Kragic Centre for Autonomous Systems Computer Vision and Active Perception Lab CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

Understanding and representing objects and their function is a challenging task. Objects we manipulate in our daily activities can be described and categorized in various ways according to their properties or affordances, depending also on our perception of those. In this work, we are interested in representing the knowledge acquired through interaction with objects, describing these in terms of action-effect relations, i.e. sensorimotor contingencies, rather than static shape or appearance representations. We demonstrate how a robot learns sensorimotor contingencies through pushing using a probabilistic model. We show how functional categories can be discovered and how entropy-based action selection can improve object classification.

关键词： Robot sensing systems Shape Data models Predictive models Gaussian processes Uncertainty

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