Learning how to grasp based on neural network retraining

Pedro, Leonardo Marquez; Belini, Valdinei Luís; Caurin, Glauco Augusto de Paula

doi:10.1080/01691864.2013.785472

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Many grasping methods already exist, several of them having been reviewed in References [4][5][6]. Two types of approaches can be distinguished: (i) the empirical approach [7,8], based on the behaviour of the human hand; and (ii) the analytical approach [9,10], based on the physical and mechanical properties of grasps. The approaches can also be classified according to the degree of a priori knowledge of the grasped objects that is available.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Grasping of Unknown Objects from a Random Pile

Sauvet

Lévesque²,

Park³

et al. 2019

Robotics

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Grasping an unknown object in a pile is no easy task for a robot—it is often difficult to distinguish different objects; objects occlude one another; object proximity limits the number of feasible grasps available; and so forth. In this paper, we propose a simple approach to grasping unknown objects one by one from a random pile. The proposed method is divided into three main actions—over-segmentation of the images, a decision algorithm and ranking according to a grasp robustness index. Thus, the robot is able to distinguish the objects from the pile, choose the best candidate for grasping among these objects, and pick the most robust grasp for this candidate. With this approach, we can clear out a random pile of unknown objects, as shown in the experiments reported herein.

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Section: Introductionmentioning

confidence: 99%

Model-Based Grasping of Unknown Objects from a Random Pile

Sauvet

Lévesque²,

Park³

et al. 2019

Robotics

View full text Add to dashboard Cite

show abstract

“…Two approaches have been used to solve this problem (Sahbani et al 2012 ; Mishra and Silver 1989 ): an empirical (physiological) approach, trying to mimic the behavior of the human hand (Feix et al 2009 ; Cutkosky 1989 ), and an analytical (mechanical) approach, considering the physical and mechanical properties involved in grasping (Shimoga 1996 ). The empirical grasp synthesis chooses the most appropriate hand configuration for the object and task to be performed using tools such as learning by demonstration (Aleotti and Caselli 2010 ; Jakel et al 2010 ; Kroemer et al 2010 ), neural networks (Pedro et al 2013 ; Leoni et al 1998 ), fuzzy logic (Bowers and Lumia 2003 ), or knowledge-based systems (Bekey et al 1993 ). Analytical grasp synthesis relies on mathematical models of the interaction between the object and the hand.…”

Section: Introductionmentioning

confidence: 99%

Grasp quality measures: review and performance

2014

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The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. The quality measures are classified into two groups according to the main aspect they evaluate: location of contact points on the object and hand configuration. The approaches that combine different measures from the two previous groups to obtain a global quality measure are also reviewed, as well as some measures related to human hand studies and grasp performance. Several examples are presented to illustrate and compare the performance of the reviewed measures.

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