Relational Affordance Learning for Task-Dependent Robot Grasping

被引：2
|
作者：
Antanas, Laura [1 ]
Dries, Anton [1 ]
Moreno, Plinio [2 ]
De Raedt, Luc [1 ]
机构：
[1] Katholieke Univ Leuven, Dept Comp Sci, Leuven, Belgium
[2] Univ Lisbon, Inst Syst & Robot, IST, Lisbon, Portugal
来源：
INDUCTIVE LOGIC PROGRAMMING (ILP 2017) | 2018年 / 10759卷
关键词：
D O I：
10.1007/978-3-319-78090-0_1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Robot grasping depends on the specific manipulation scenario: the object, its properties, task and grasp constraints. Object-task affordances facilitate semantic reasoning about pre-grasp configurations with respect to the intended tasks, favoring good grasps. We employ probabilistic rule learning to recover such object-task affordances for task-dependent grasping from realistic video data.
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页码：1 / 15
页数：15
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