Part-Based Grasp Planning For Familiar Objects
2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)(2016)
摘要
In this work, we present a part-based grasp planning approach that is capable of generating grasps that are applicable to multiple familiar objects.We show how object models can be decomposed according to their shape and local volumetric information. The resulting object parts are labeled with semantic information and used for generating robotic grasping information. We investigate how the transfer of such grasping information to familiar objects can be achieved and how the transferability of grasps can be measured. We show that the grasp transferability measure provides valuable information about how successful planned grasps can be applied to novel object instances of the same object category.We evaluate the approach in simulation, by applying it to multiple object categories and determine how successful the planned grasps can be transferred to novel, but familiar objects. In addition, we present a use case on the humanoid robot ARMAR-III.
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关键词
ARMAR-III humanoid robot,grasp transferability,robotic grasping information generation,semantic information,local volumetric information,familiar objects,part-based grasp planning
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