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

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Learning the tactile signatures of prototypical object parts for robust part-based grasping of novel objects

Learning the tactile signatures of prototypical object parts...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Emil Hyttinen Danica Kragic Renaud Detry the Centre for Autonomous Systems and the Computer Vision and Active Perception Lab KTH Royal Institute of Technology Stockholm Sweden the University of Liège Belgium

We present a robotic agent that learns to derive object grasp stability from touch. The main contribution of our work is the use of a characterization of the shape of the part of the object that is enclosed by the gripper to condition the tactile-based stability model. As a result, the agent is able to express that a specific tactile signature may for instance indicate stability when grasping a cylinder, while cuing instability when grasping a box. We proceed by (1) discretizing the space of graspable object parts into a small set of prototypical shapes, via a data-driven clustering process, and (2) learning a touch-based stability classifier for each prototype. Classification is conducted through kernel logistic regression, applied to a low-dimensional approximation of the tactile data read from the robot's hand. We present an experiment that demonstrates the applicability of the method, yielding a success rate of 89%. Our experiment also shows that the distribution of tactile data differs substantially between grasps collected with different prototypes, supporting the use of shape cues in touch-based stability estimators.

关键词： Shape Prototypes Stability analysis Joints Grasping Robot sensing systems

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Object recognition using saliency maps and HTM learning

Object recognition using saliency maps and HTM learning

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2012 IEEE International Conference on Imaging systems and Techniques, IST 2012

作者： Kostavelis, Ioannis Nalpantidis, Lazaros Gasteratos, Antonios Laboratory of Robotics and Automation Department of Production and Management Engineering Democritus University of Thrace Vas. Sofias 12 GR-67100 Xanthi Greece Computer Vision and Active Perception Lab. Centre for Autonomous Systems Royal Institute of Technology - KTH SE-100 44 Stockholm Sweden

ISBN: (纸本)9781457717741

In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTM's biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems. © 2012 IEEE.

关键词： Object recognition

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Estimating the Deformability of Elastic Materials using Optical Flow and Position-based Dynamics

Estimating the Deformability of Elastic Materials using Opti...

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IEEE-RAS International Conference on Humanoid Robots

作者： Puren Guler Karl Pauwels Alessandro Pieropan Hedvig Kjellstrom Danica Kragic Computer Vision and Active Perception Lab Center for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology

ISBN: (纸本)9781479968862

Knowledge of the physical properties of objects is essential in a wide range of robotic manipulation scenarios. A robot may not always be aware of such properties prior to interaction. If an object is incorrectly assumed to be rigid, it may exhibit unpredictable behavior when grasped. In this paper, we use vision based observation of the behavior of an object a robot is interacting with and use it as the basis for estimation of its elastic deformability. This is estimated in a local region around the interaction point using a physics simulator. We use optical flow to estimate the parameters of a position-based dynamics simulation using meshless shape matching (MSM). MSM has been widely used in computer graphics due to its computational efficiency, which is also important for closed-loop control in robotics. In a controlled experiment we demonstrate that our method can qualitatively estimate the physical properties of objects with different degrees of deformability.

关键词： Deformability Optic Flow Unpredictable behavior physical properties elastic materials Behavior Observation Techniques Robot kinetic simulation computer GRAPHICS MSM Computational efficiency robotics

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Long-Term Topological Localisation for Service Robots in Dynamic Environments using Spectral Maps

Long-Term Topological Localisation for Service Robots in Dyn...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Tomas Krajnik Jaime P. Fentanes Oscar M. Mozos Tom Duckett Johan Ekekrantz Marc Hanheide Lincoln Centre for Autonomous Systems University of Lincoln United Kingdom Computer Vision and Active Perception Lab The Royal Institute of Technology (KTH) Sweden

ISBN: (纸本)9781479969357

This paper presents a new approach for topological localisation of service robots in dynamic indoor environments. In contrast to typical localisation approaches that rely mainly on static parts of the environment, our approach makes explicit use of information about changes by learning and modelling the spatio-temporal dynamics of the environment where the robot is acting. The proposed spatio-temporal world model is able to predict environmental changes in time, allowing the robot to improve its localisation capabilities during long-term operations in populated environments. To investigate the proposed approach, we have enabled a mobile robot to autonomously patrol a populated environment over a period of one week while building the proposed model representation. We demonstrate that the experience learned during one week is applicable for topological localization even after a hiatus of three months by showing that the localization error rate is significantly lower compared to static environment representations.

关键词： Service robots Environments Robot indoor environment Mobile robots Environmental changes Explicit

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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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Predicting slippage and learning manipulation affordances through Gaussian Process regression

Predicting slippage and learning manipulation affordances th...

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IEEE-RAS International Conference on Humanoid Robots

作者： Francisco E. Viña B. Yasemin Bekiroglu Christian Smith Yiannis Karayiannidis 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 Computer Vision and Active Perception Lab Royal Institute of Technology KTH Stockholm Sweden

Object grasping is commonly followed by some form of object manipulation - either when using the grasped object as a tool or actively changing its position in the hand through in-hand manipulation to afford further interaction. In this process, slippage may occur due to inappropriate contact forces, various types of noise and/or due to the unexpected interaction or collision with the environment. In this paper, we study the problem of identifying continuous bounds on the forces and torques that can be applied on a grasped object before slippage occurs. We model the problem as kinesthetic rather than cutaneous learning given that the measurements originate from a wrist mounted force-torque sensor. Given the continuous output, this regression problem is solved using a Gaussian Process approach. We demonstrate a dual armed humanoid robot that can autonomously learn force and torque bounds and use these to execute actions on objects such as sliding and pushing. We show that the model can be used not only for the detection of maximum allowable forces and torques but also for potentially identifying what types of tasks, denoted as manipulation affordances, a specific grasp configuration allows. The latter can then be used to either avoid specific motions or as a simple step of achieving in-hand manipulation of objects through interaction with the environment.

关键词： Friction Robot sensing systems Force Torque Grasping

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Grasping objects with holes: A topological approach

Grasping objects with holes: A topological approach

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Florian T. Pokorny Johannes A. Stork Danica Kragic Computer Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

This work proposes a topologically inspired approach for generating robot grasps on objects with `holes'. Starting from a noisy point-cloud, we generate a simplicial representation of an object of interest and use a recently developed method for approximating shortest homology generators to identify graspable loops. To control the movement of the robot hand, a topologically motivated coordinate system is used in order to wrap the hand around such loops. Finally, another concept from topology - namely the Gauss linking integral - is adapted to serve as evidence for secure caging grasps after a grasp has been executed. We evaluate our approach in simulation on a Barrett hand using several target objects of different sizes and shapes and present an initial experiment with real sensor data.

关键词： Noise measurement Robots Jacobian matrices

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Robot navigation under uncertainties using event based sampling

Robot navigation under uncertainties using event based sampl...

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IEEE Conference on Decision and Control

作者： Michele Colledanchise Dimos V. Dimarogonas Petter Ögren Centre for Autonomous Systems The Royal Institute of Technology - KTH Stockholm Sweden Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication The Royal Institute of Technology - KTH Stockholm Sweden

In many robot applications, sensor feedback is needed to reduce uncertainties in environment models. However, sensor data acquisition also induces costs in terms of the time elapsed to make the observations and the computations needed to find new estimates. In this paper, we show how to use event based sampling to reduce the number of measurements done, thereby saving time, computational resources and power, without jeopardizing critical system properties such as safety and goal convergence. This is done by combining recent advances in nonlinear estimation with event based control using artificial potential fields. The results are particularly useful for real time systems such as high speed vehicles or teleoperated robots, where the cost of taking measurements is even higher, in terms of stops or transmission times. We conclude the paper with a set of simulations to illustrate the effectiveness of the approach and compare it with a baseline approach using periodic measurements.

关键词： Robot sensing systems Collision avoidance Uncertainty Time measurement Bismuth Navigation

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Layered HMM for Motion Intention Recognition

Layered HMM for Motion Intention Recognition

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2006 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Daniel Aarno Danica Kragic School of Computer Science and Communication Centre for Autonomous Systems-Computational Vision and Active Perception Laboratory Royal Institute of Technology Sweden

Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtask in order to provide manageable modeling. In this paper we consider the use of a layered hidden Markov model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classification performance with respect to noise, number of gestems, type of HMM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi-dimensional HMMs for gestem classification are small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers

关键词： Hidden Markov models Intelligent robots Man machine systems Collaboration Learning Human robot interaction Robotics and automation Speech recognition Character recognition computer vision

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YES - YEt another object segmentation: Exploiting camera movement

YES - YEt another object segmentation: Exploiting camera mov...

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2012 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Lazaros Nalpantidis Mårten Björkman Danica Kragic Computer Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology (KTH) Stockholm Sweden

We address the problem of object segmentation in image sequences where no a-priori knowledge of objects is assumed. We take advantage of robots' ability to move, gathering multiple images of the scene. Our approach starts by extracting edges, uses a polar domain representation and performs integration over time based on a simple dilation operation. The proposed system can be used for providing reliable initial segmentation of unknown objects in scenes of varying complexity, allowing for recognition, categorization or physical interaction with the objects. The experimental evaluation on both self-captured and a publicly available dataset shows the efficiency and stability of the proposed method.

关键词： Image edge detection Image segmentation Object segmentation Correlation Optical imaging Robots Detectors

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