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检索条件"机构=Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory"

共 71 条记录，以下是11-20 订阅

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Combination of foveal and peripheral vision for object recognition and pose estimation

Combination of foveal and peripheral vision for object recog...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： M. Bjorkman D. Kragic Centre for Autonomous Systems & Computer Vision and Active Perception Laboratory Royal Institute of Technology Stockholm Sweden

In this paper, we present a real-time vision system that integrates a number of algorithms using monocular and binocular cues to achieve robustness in realistic settings, for tasks such as object recognition, tracking and pose estimation. The system consists of two sets of binocular cameras; a peripheral set for disparity based attention and a foveal one for higher level processes. Thus the conflicting requirements of a wide field of view and high resolution can be overcome. One important property of the system is that the step from task specification through object recognition to pose estimation is completely automatic, combining both appearance and geometric models. Experimental evaluation is performed in a realistic indoor environment with occlusions, clutter, changing lighting and background conditions.

关键词： Object recognition Cameras Machine vision Robustness Layout Image segmentation Image recognition computer vision Real time systems Solid modeling

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Representations for object grasping and learning from experience

Representations for object grasping and learning from experi...

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2010 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Oscar J. Rubio Kai Huebner Danica Kragic Computer Vision and Active Perception Laboratory Centre of Autonomous Systems School of Computer Science and Communication KTH Stockholm Sweden

We study two important problems in the area of robot grasping: i) the methodology and representations for grasp selection on known and unknown objects, and ii) learning from experience for grasping of similar objects. The core part of the paper is the study of different representations necessary for implementing grasping tasks on objects of different complexity. We show how to select a grasp satisfying force-closure, taking into account the parameters of the robot hand and collision-free paths. Our implementation takes also into account efficient computation at different levels of the system regarding representation, description and grasp hypotheses generation.

关键词： Clouds Shape Three dimensional displays Robots Databases Computational modeling Grasping

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A probabilistic framework for task-oriented grasp stability assessment

A probabilistic framework for task-oriented grasp stability ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yasemin Bekiroglu Dan Song Lu Wang Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

We present a probabilistic framework for grasp modeling and stability assessment. The framework facilitates assessment of grasp success in a goal-oriented way, taking into account both geometric constraints for task affordances and stability requirements specific for a task. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot's self-exploration. The conditional relations between tasks and multiple sensory streams (vision, proprioception and tactile) are modeled using Bayesian networks. The generative modeling approach both allows prediction of grasp success, and provides insights into dependencies between variables and features relevant for object grasping.

关键词： Robot sensing systems Bayes methods Stability analysis Probabilistic logic Planning Grasping

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Learning a dictionary of prototypical grasp-predicting parts from grasping experience

Learning a dictionary of prototypical grasp-predicting parts...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Renaud Detry Carl Henrik Ek Marianna Madry Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition of a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We present an experiment in which our agent extracts five prototypical parts from thirty-two real-world grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects.

关键词： Robots Shape Prototypes Three-dimensional displays Grasping Dictionaries Training

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Learning and Recognition of Object Manipulation Actions Using Linear and Nonlinear Dimensionality Reduction

Learning and Recognition of Object Manipulation Actions Usin...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Isabel Serrano Vicente Danica Kragic Jan-Olof Eklundh Computational Vision and Active Perception Laboratory and Centre for Autonomous Systems KTH Stockholm Sweden

In this work, we perform an extensive statistical evaluation for learning and recognition of object manipulation actions. We concentrate on single arm/hand actions but study the problem of modeling and dimensionality reduction for cases where actions are very similar to each other in terms of arm motions. For this purpose, we evaluate a linear and a nonlinear dimensionality reduction techniques: principal component analysis and spatio-temporal isomap. Classification of query sequences is based on different variants of Nearest Neighbor classification. We thoroughly describe and evaluate different parameters that affect the modeling strategies and perform the evaluation with a training set of 20 people.

关键词： Robot kinematics Performance evaluation Principal component analysis Nearest neighbor searches Educational robots Learning systems Human robot interaction Robot vision systems computer vision Vocabulary

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Sparse summarization of robotic grasping data

Sparse summarization of robotic grasping data

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Martin Hjelm Carl Henrik Ek Renaud Detry Hedvig Kjellström Danica Kragic Centre for Autonomous Systems Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios.

关键词： Robots Data models Grasping Shape Kernel Optimization Encoding

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Visual recognition of grasps for human-to-robot mapping

Visual recognition of grasps for human-to-robot mapping

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2008 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Hedvig Kjellstrom Javier Romero Danica Kragic Computational Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication KTH Stockholm Sweden

ISBN: (纸本)9781424420575

This paper presents a vision based method for grasp classification. It is developed as part of a Programming by Demonstration (PbD) system for which recognition of objects and pick-and-place actions represent basic building blocks for task learning. In contrary to earlier approaches, no articulated 3D reconstruction of the hand over time is taking place. The indata consists of a single image of the human hand. A 2D representation of the hand shape, based on gradient orientation histograms, is extracted from the image. The hand shape is then classified as one of six grasps by finding similar hand shapes in a large database of grasp images. The database search is performed using Locality Sensitive Hashing (LSH), an approximate k-nearest neighbor approach. The nearest neighbors also give an estimated hand orientation with respect to the camera. The six human grasps are mapped to three Barret hand grasps. Depending on the type of robot grasp, a precomputed grasp strategy is selected. The strategy is further parameterized by the orientation of the hand relative to the object. To evaluate the potential for the method to be part of a robust vision system, experiments were performed, comparing classification results to a baseline of human classification performance. The experiments showed the LSH recognition performance to be comparable to human performance.

关键词： Robots Three dimensional displays Image segmentation Image reconstruction Humans Robot sensing systems Distance measurement

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Improving generalization for 3D object categorization with Global Structure Histograms

Improving generalization for 3D object categorization with G...

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2012 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Marianna Madry Carl Henrik Ek Renaud Detry Kaiyu Hang Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467317375

We propose a new object descriptor for three dimensional data named the Global Structure Histogram (GSH). The GSH encodes the structure of a local feature response on a coarse global scale, providing a beneficial trade-off between generalization and discrimination. Encoding the structural characteristics of an object allows us to retain low local variations while keeping the benefit of global representativeness. In an extensive experimental evaluation, we applied the framework to category-based object classification in realistic scenarios. We show results obtained by combining the GSH with several different local shape representations, and we demonstrate significant improvements to other state-of-the-art global descriptors.

关键词： Histograms Robustness Encoding Databases Image color analysis Shape Data models

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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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Model-free robot manipulation of doors and drawers by means of fixed-grasps

Model-free robot manipulation of doors and drawers by means ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yiannis Karayiannidis Christian Smith Francisco E. Viña Petter Ögren Danica Kragic Computer Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology Stockholm Sweden

This paper addresses the problem of robot interaction with objects attached to the environment through joints such as doors or drawers. We propose a methodology that requires no prior knowledge of the objects' kinematics, including the type of joint - either prismatic or revolute. The method consists of a velocity controller which relies on force/torque measurements and estimation of the motion direction, rotational axis and the distance from the center of rotation. The method is suitable for any velocity controlled manipulator with a force/torque sensor at the end-effector. The force/torque control regulates the applied forces and torques within given constraints, while the velocity controller ensures that the end-effector moves with a task-related desired tangential velocity. The paper also provides a proof that the estimates converge to the actual values. The method is evaluated in different scenarios typically met in a household environment.

关键词： Torque Force Robots Hardware

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