Relational Affordance Learning for Task-Dependent Robot Grasping View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-03-15

AUTHORS

Laura Antanas , Anton Dries , Plinio Moreno , Luc De Raedt

ABSTRACT

Robot grasping depends on the specific manipulation scenario: the object, its properties, task and grasp constraints. Object-task affordances facilitate semantic reasoning about pre-grasp configurations with respect to the intended tasks, favoring good grasps. We employ probabilistic rule learning to recover such object-task affordances for task-dependent grasping from realistic video data.

PAGES

1-15

References to SciGraph publications

  • 2011. Probabilistic Rule Learning in INDUCTIVE LOGIC PROGRAMMING
  • 2013. Opening Doors: An Initial SRL Approach in INDUCTIVE LOGIC PROGRAMMING
  • 2015. ProbLog2: Probabilistic Logic Programming in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2018-01. Relational affordances for multiple-object manipulation in AUTONOMOUS ROBOTS
  • Book

    TITLE

    Inductive Logic Programming

    ISBN

    978-3-319-78089-4
    978-3-319-78090-0

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-78090-0_1

    DOI

    http://dx.doi.org/10.1007/978-3-319-78090-0_1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1101580107


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