Digitala Vetenskapliga Arkivet

Change search
Refine search result
1 - 8 of 8
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Bekiroglu, Y.
    et al.
    Damianou, A.
    Detry, Renaud
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. University of Liège.
    Stork, Johannes A.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ek, Carl Henrik
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. University of Bristol.
    Probabilistic consolidation of grasp experience2016In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2016, p. 193-200Conference paper (Refereed)
    Abstract [en]

    We present a probabilistic model for joint representation of several sensory modalities and action parameters in a robotic grasping scenario. Our non-linear probabilistic latent variable model encodes relationships between grasp-related parameters, learns the importance of features, and expresses confidence in estimates. The model learns associations between stable and unstable grasps that it experiences during an exploration phase. We demonstrate the applicability of the model for estimating grasp stability, correcting grasps, identifying objects based on tactile imprints and predicting tactile imprints from object-relative gripper poses. We performed experiments on a real platform with both known and novel objects, i.e., objects the robot trained with, and previously unseen objects. Grasp correction had a 75% success rate on known objects, and 73% on new objects. We compared our model to a traditional regression model that succeeded in correcting grasps in only 38% of cases.

  • 2.
    Bekiroglu, Yasemin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Detry, Renaud
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Joint Observation of Object Pose and Tactile Imprints for Online Grasp Stability Assessment2011Conference paper (Refereed)
    Abstract [en]

    This paper studies the viability of concurrentobject pose tracking and tactile sensing for assessing graspstability on a physical robotic platform. We present a kernellogistic-regression model of pose- and touch-conditional graspsuccess probability. Models are trained on grasp data whichconsist of (1) the pose of the gripper relative to the object,(2) a tactile description of the contacts between the objectand the fully-closed gripper, and (3) a binary descriptionof grasp feasibility, which indicates whether the grasp canbe used to rigidly control the object. The data is collectedby executing grasps demonstrated by a human on a roboticplatform composed of an industrial arm, a three-finger gripperequipped with tactile sensing arrays, and a vision-based objectpose tracking system. The robot is able to track the poseof an object while it is grasping it, and it can acquiregrasp tactile imprints via pressure sensor arrays mounted onits gripper’s fingers. We consider models defined on severalsubspaces of our input data – using tactile perceptions orgripper poses only. Models are optimized and evaluated with f-fold cross-validation. Our preliminary results show that stabilityassessments based on both tactile and pose data can providebetter rates than assessments based on tactile data alone.

  • 3.
    Bekiroglu, Yasemin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Detry, Renaud
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Learning Tactile Characterizations Of Object- And Pose-specific Grasps2011Conference paper (Refereed)
    Abstract [en]

    Our aim is to predict the stability of a grasp from the perceptions available to a robot before attempting to lift up and transport an object. The percepts we consider consist of the tactile imprints and the object-gripper configuration read before and until the robot’s manipulator is fully closed around an object. Our robot is equipped with multiple tactile sensing arrays and it is able to track the pose of an object during the application of a grasp. We present a kernel-logistic-regression model of pose- and touch-conditional grasp success probability which we train on grasp data collected by letting the robot experience the effect on tactile and visual signals of grasps suggested by a teacher, and letting the robot verify which grasps can be used to rigidly control the object. We consider models defined on several subspaces of our input data – e.g., using tactile perceptions or pose information only. Our experiment demonstrates that joint tactile and pose-based perceptions carry valuable grasp-related information, as models trained on both hand poses and tactile parameters perform better than the models trained exclusively on one perceptual input.

  • 4. Bodenhagen, L.
    et al.
    Detry, Renaud
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Piater, J.
    Krüger, N.
    What a successful grasp tells about the success chances of grasps in its vicinity2011Conference paper (Refereed)
    Abstract [en]

    Infants gradually improve their grasping competences, both in terms of motor abilities as well as in terms of the internal shape grasp representations. Grasp densities [3] provide a statistical model of such an internal learning process. In the concept of grasp densities, kernel density estimation is used based on a six-dimensional kernel representing grasps with given position and orientation. For this so far an isotropic kernel has been used which exact shape have only been weakly justified. Instead in this paper, we use an anisotropic kernel that is statistically based on measured conditional probabilities representing grasp success in the neighborhood of a successful grasp. The anisotropy has been determined utilizing a simulation environment that allowed for evaluation of large scale experiments. The anisotropic kernel has been fitted to the conditional probabilities obtained from the experiments. We then show that convergence is an important problem associated with the grasp density approach and we propose a measure for the convergence of the densities. In this context, we show that the use of the statistically grounded anisotropic kernels leads to a significantly faster convergence of grasp densities.

  • 5.
    Detry, Renaud
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ek, Carl Henrik
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Madry, Marianna
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Learning a dictionary of prototypical grasp-predicting parts from grasping experience2013In: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, p. 601-608Conference paper (Refereed)
    Abstract [en]

    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.

  • 6.
    Detry, Renaud
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ek, Carl Henrik
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Madry, Marianna
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Piater, Justus
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Generalizing grasps across partly similar objects2012In: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2012, p. 3791-3797Conference paper (Refereed)
    Abstract [en]

    The paper starts by reviewing the challenges associated to grasp planning, and previous work on robot grasping. Our review emphasizes the importance of agents that generalize grasping strategies across objects, and that are able to transfer these strategies to novel objects. In the rest of the paper, we then devise a novel approach to the grasp transfer problem, where generalization is achieved by learning, from a set of grasp examples, a dictionary of object parts by which objects are often grasped. We detail the application of dimensionality reduction and unsupervised clustering algorithms to the end of identifying the size and shape of parts that often predict the application of a grasp. The learned dictionary allows our agent to grasp novel objects which share a part with previously seen objects, by matching the learned parts to the current view of the new object, and selecting the grasp associated to the best-fitting part. We present and discuss a proof-of-concept experiment in which a dictionary is learned from a set of synthetic grasp examples. While prior work in this area focused primarily on shape analysis (parts identified, e.g., through visual clustering, or salient structure analysis), the key aspect of this work is the emergence of parts from both object shape and grasp examples. As a result, parts intrinsically encode the intention of executing a grasp.

  • 7.
    Hjelm, Martin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ek, Carl Henrik
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Detry, Renaud
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kjellström, Hedvig
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Sparse Summarization of Robotic Grasping Data2013In: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, p. 1082-1087Conference paper (Refereed)
    Abstract [en]

    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.

  • 8.
    Madry, Marianna
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ek, Carl Henrik
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Detry, Renaud
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hang, Kaiyu
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Improving Generalization for 3D Object Categorization with Global Structure Histograms2012In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE conference proceedings, 2012, p. 1379-1386Conference paper (Refereed)
    Abstract [en]

    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.

1 - 8 of 8
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf