Browsing by Author "Hsu, Keng-Fu"

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Multi-step Pick-and-Place Tasks Using Object-centric Dense Correspondences
(2019-01-01)
Chai, Chun-Yu
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Hsu, Keng-Fu
;
Tsao, Shiao-Li
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資訊科學與工程研究所
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Institute of Computer Science and Engineering
This paper presents an object-centric method for efficiently performing two types of challenging pick-and-place tasks, namely sequential pick and place and object sorting. We propose multiclass dense object nets (MCDONs) for learning object-centric dense descriptors that maintain not only intraclass variations but also inter-class separation. Intra-class consistency is also inherently learned and is useful for our pick-and-place tasks. All the tasks only require a single demonstration from users, which can then be generalized to all class instances. A dataset containing eight classes and a total of 52 objects was provided in this study. We obtained a task success rate of 93.33% on a five-block stacking task and 97.41% on a three-class object sorting task.