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  • 1.
    Cruciani, Silvia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Hang, Yin
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    In-Hand Manipulation of Objects with Unknown ShapesManuscript (preprint) (Other academic)
    Abstract [en]

    This work addresses the problem of changing grasp configurations on objects with an unknown shape through in-hand manipulation. Our approach leverages shape priors,learned as deep generative models, to infer novel object shapesfrom partial visual sensing. The Dexterous Manipulation Graph method is extended to build upon incremental data and account for estimation uncertainty in searching a sequence of manipulation actions. We show that our approach successfully solves in-hand manipulation tasks with unknown objects, and demonstrate the validity of these solutions with robot experiments.

  • 2.
    Cruciani, Silvia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Yin, Hang
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    In-Hand Manipulation of Objects with Unknown ShapesManuscript (preprint) (Other academic)
    Abstract [en]

    This work addresses the problem of changing grasp configurations on objects with an unknown shape through in-hand manipulation. Our approach leverages shape priors,learned as deep generative models, to infer novel object shapesfrom partial visual sensing. The Dexterous Manipulation Graph method is extended to build upon incremental data and account for estimation uncertainty in searching a sequence of manipulation actions. We show that our approach successfully solves in-hand manipulation tasks with unknown objects, and demonstrate the validity of these solutions with robot experiments.

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