Category-based task specific grasping

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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.


Original languageEnglish
Pages (from-to)25-35
Number of pages11
JournalRobotics and Autonomous Systems
Issue number0
Publication statusPublished - 1 Aug 2015
MoE publication typeA1 Journal article-refereed

    Research areas

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

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