Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 24, Issue 11, Pages 13133-13140Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3225322
Keywords
Computer vision; contrastive learning; deformable object; latent space; shape control
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In recent years, the methods of loading and transporting rigid objects have improved. However, controlling the shape of deformable objects during transportation has attracted attention. This study uses contrastive learning to solve the shape control problem of deformable objects and improves the model's representation ability by constructing an encoder to extract effective information. Experimental results show significant performance improvements compared to baseline methods.
In recent years, the methods of loading and transporting rigid objects have become more and more perfect. However, in the process of transportation, the shape control of deformable objects has attracted extensive attention because deformable objects have been widely used in intelligent tasks such as packing and sorting cables before transportation. Restricted by the super-degrees of freedom and nonlinear dynamic models of deformable objects, planning the action trajectories to control the shape of deformable objects is a challenging task. In this work, we use contrastive learning to solve the shape control problem of deformable objects. The method jointly optimizes the visual representation model and dynamic model of deformable objects, maps the target nonlinear state to linear latent space which avoids model inference for deformable objects in infinite-dimensional configuration spaces. Furthermore, to extract effective information in the latent space, we construct an encoder with a multi-branch topology to improve the representation ability of the model. Experimentally, we collect dynamic trajectory data for random shape control task involving cloth or rope in a simulated environment. Then we apply it to train the proposed offline method to obtain latent dynamic models for shape control of deformable objects. In comparison with other baseline methods, our proposed method achieves substantial performance improvements.
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