Deeply Supervised Subspace Learning for Cross-Modal Material Perception of Known and Unknown Objects

Document Type

Article

Publication Date

2-1-2023

Abstract

In order to help robots understand and perceive an object's properties during noncontact robot-object interaction, this article proposes a deeply supervised subspace learning method. In contrast to previous work, it takes the advantages of low noise and fast response of noncontact sensors and extracts novel contactless feature information to retrieve cross-modal information, so as to estimate and infer material properties of known as well as unknown objects. Specifically, a depth-supervised subspace cross-modal material retrieval model is trained to learn a common low-dimensional feature representation to capture the clustering structure among different modal features of the same class of objects. Meanwhile, all of unknown objects are accurately perceived by an energy-based model, which forces an unlabeled novel object's features to be mapped beyond the common low-dimensional features. The experimental results show that our approach is effective in comparison with other advanced methods.

Identifier

85135761047 (Scopus)

Publication Title

IEEE Transactions on Industrial Informatics

External Full Text Location

https://doi.org/10.1109/TII.2022.3195171

e-ISSN

19410050

ISSN

15513203

First Page

2259

Last Page

2268

Issue

2

Volume

19

Grant

20204BCJ23006

Fund Ref

National Natural Science Foundation of China

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