Category-based task specific grasping

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openAccess

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Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2015-08-01

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Mcode

Degree programme

Language

en

Pages

11
25-35

Series

ROBOTICS AND AUTONOMOUS SYSTEMS, Volume 70, issue 0

Abstract

The problem of finding stable grasps has been widely studied in robotics. However, in many applications the resulting grasps should not only be stable but also applicable for a particular task. Task-specific grasps are closely linked to object categories so that objects in a same category can be often used to perform the same task. This paper presents a probabilistic approach for task-specific stable grasping of objects with shape variations inside the category. An optimal grasp is found as a grasp that is maximally likely to be task compatible and stable taking into account shape uncertainty in a probabilistic context. The method requires only partial models of new objects for grasp generation and only few models and example grasps are used during the training stage. The experiments show that the approach can use multiple models to generalize to new objects in that it outperforms grasping based on the closest model. The method is shown to generate stable grasps for new objects belonging to the same class as well as for similar in shape objects of different categories.

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Keywords

Category-based grasping, Probabilistic grasping, Shape uncertainty, Task-specific grasping

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Citation

Nikandrova, E & Kyrki, V 2015, ' Category-based task specific grasping ', Robotics and Autonomous Systems, vol. 70, no. 0, pp. 25-35 . https://doi.org/10.1016/j.robot.2015.04.002