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Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task
[article]
2022
arXiv
pre-print
Robotic pick-and-place has been researched for a long time to cope with uncertainty of novel objects and changeable environments. Past works mainly focus on learning-based methods to achieve high precision. However, they have difficulty being generalized for the limitation of specified training models. To break through this drawback of learning-based approaches, we introduce a new perspective of similarity matching between novel objects and a known database based on category-association to
arXiv:2201.08177v1
fatcat:a2rnq5zhgfcubbbxeyfky6tuea