[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Hyttinen et al., 2017 - Google Patents

Estimating tactile data for adaptive grasping of novel objects

Hyttinen et al., 2017

View PDF
Document ID
4259562193726364322
Author
Hyttinen E
Kragic D
Detry R
Publication year
Publication venue
2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)

External Links

Snippet

We present an adaptive grasping method that finds stable grasps on novel objects. The main contributions of this paper is in the computation of the probability of success of grasps in the vicinity of an already applied grasp. Our method performs adaptions by simulating …
Continue reading at www.csc.kth.se (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268Feature extraction; Face representation
    • G06K9/00281Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Calandra et al. More than a feeling: Learning to grasp and regrasp using vision and touch
Chebotar et al. Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning
Herzog et al. Learning of grasp selection based on shape-templates
Ciocarlie et al. Towards reliable grasping and manipulation in household environments
Li et al. Learning of grasp adaptation through experience and tactile sensing
Dang et al. Stable grasping under pose uncertainty using tactile feedback
Dang et al. Learning grasp stability
US11185986B2 (en) Robotic fingertip design and grasping on contact primitives
Hyttinen et al. Learning the tactile signatures of prototypical object parts for robust part-based grasping of novel objects
Goins et al. Evaluating the efficacy of grasp metrics for utilization in a gaussian process-based grasp predictor
Hyttinen et al. Estimating tactile data for adaptive grasping of novel objects
Nikandrova et al. Category-based task specific grasping
Liang et al. In-hand object pose tracking via contact feedback and gpu-accelerated robotic simulation
Sutanto et al. Learning latent space dynamics for tactile servoing
Ottenhaus et al. Visuo-haptic grasping of unknown objects based on gaussian process implicit surfaces and deep learning
Von Drigalski et al. Contact-based in-hand pose estimation using bayesian state estimation and particle filtering
Hoffmann et al. Adaptive robotic tool use under variable grasps
Faria et al. Knowledge-based reasoning from human grasp demonstrations for robot grasp synthesis
Nikandrova et al. Towards informative sensor-based grasp planning
Eppner et al. Visual detection of opportunities to exploit contact in grasping using contextual multi-armed bandits
Strub et al. Using haptics to extract object shape from rotational manipulations
Funabashi et al. Tactile transfer learning and object recognition with a multifingered hand using morphology specific convolutional neural networks
Abu-Dakka et al. Force-based learning of variable impedance skills for robotic manipulation
Nguyen et al. A probabilistic framework for tracking uncertainties in robotic manipulation
Khadivar et al. Online active and dynamic object shape exploration with a multi-fingered robotic hand