Relational Affordance Learning for Task-Dependent Robot Grasping
نویسندگان
چکیده
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, favouring 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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تاریخ انتشار 2017