563 research outputs found
Pick and Place Without Geometric Object Models
We propose a novel formulation of robotic pick and place as a deep
reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic
manipulation frame the problem in terms of low level states and actions, we
propose a more abstract formulation. In this formulation, actions are target
reach poses for the hand and states are a history of such reaches. We show this
approach can solve a challenging class of pick-place and regrasping problems
where the exact geometry of the objects to be handled is unknown. The only
information our method requires is: 1) the sensor perception available to the
robot at test time; 2) prior knowledge of the general class of objects for
which the system was trained. We evaluate our method using objects belonging to
two different categories, mugs and bottles, both in simulation and on real
hardware. Results show a major improvement relative to a shape primitives
baseline
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
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Robot learning of everyday object manipulations via human demonstration
We deal with the problem of teaching a robot to manipulate everyday objects through human demonstration. We first design a task descriptor which encapsulates important elements of a task. The design originates from observations that manipulations involved in many everyday object tasks can be considered as a series of sequential rotations and translations, which we call manipulation primitives. We then propose a method that enables a robot to decompose a demonstrated task into sequential manipulation primitives and construct a task descriptor. We also show how to transfer a task descriptor learned from one object to similar objects. In the end, we argue that this framework is highly generic. Particularly, it can be used to construct a robot task database that serves as a manipulation knowledge base for a robot to succeed in manipulating everyday objects
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