2013
DOI: 10.1007/978-3-319-00065-7_29
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A Data-Driven Statistical Framework for Post-Grasp Manipulation

Abstract: Grasping an object is usually only an intermediate goal for a robotic manipulator. To finish the task, the robot needs to know where the object is in its hand and what action to execute. This paper presents a general statistical framework to address these problems. Given a novel object, the robot learns a statistical model of grasp state conditioned on sensor values. The robot also builds a statistical model of the requirements of the task in terms of grasp state accuracy. Both of these models are constructed … Show more

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Cited by 16 publications
(18 citation statements)
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“…The approach presented by Jiang [14], [15] uses learning on hand-designed features and successfully places known objects stably 98% of the time and new objects 82% of the time. Paolini [16] estimates the probability of a successful placement, then attempts to solve for the most likely placement location, given a grasped object. Fu [17] use hand chosen features to try and find the upright orientation of man-made objects.…”
Section: Related Work a Robotic Placingmentioning
confidence: 99%
“…The approach presented by Jiang [14], [15] uses learning on hand-designed features and successfully places known objects stably 98% of the time and new objects 82% of the time. Paolini [16] estimates the probability of a successful placement, then attempts to solve for the most likely placement location, given a grasped object. Fu [17] use hand chosen features to try and find the upright orientation of man-made objects.…”
Section: Related Work a Robotic Placingmentioning
confidence: 99%
“…Gaussian Processes (GPs) are a powerful tool to model the dynamics of complex systems [5,8,9], and have been applied to different aspects of robotics including planning and control [10,11,12], system identification [8,13,14], or filtering [6,7,15]. In this work we study the problem of accurate propagation and filtering of the state of a stochastic dynamic system.…”
Section: Related Workmentioning
confidence: 99%
“…Gaussian Processes (GPs) are a powerful tool to model the dynamics of complex systems [5,8,9], and have been applied to different aspects of robotics including planning and control [10,11,12], system identification [8,13,14], or filtering [6,7,15]. In this work we study the problem of accurate propagation and filtering of the state of a stochastic dynamic system.…”
Section: Related Workmentioning
confidence: 99%
“…distributions. Multimodality and complex belief distributions have been experimentally observed in a variety of manipulation actions such as planar pushing [2,3], ground impacts [4], and bin-picking [5].…”
Section: Introductionmentioning
confidence: 99%