Abstract
Perceiving and identifying material properties of surfaces and objects is important for us to interact with the world. Therefore, automatic material identification plays a critical role in the intelligent manufacturing systems. In many scenarios, the tactile samples and the tactile adjective descriptions about some materials can be provided. How to exploit their relation is a challenging problem. In this chapter, a semantics-regularized dictionary learning method is developed to incorporate such advanced semantic information into the training model to improve the material identification performance. A set of optimization algorithms are developed to obtain the solutions of the proposed optimization problem. Finally, extensive experimental evaluations are performed on publicly available datasets to show the effectiveness of the proposed method.
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Liu, H., Sun, F. (2018). Tactile Material Identification Using Semantics-Regularized Dictionary Learning. In: Robotic Tactile Perception and Understanding. Springer, Singapore. https://doi.org/10.1007/978-981-10-6171-4_6
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DOI: https://doi.org/10.1007/978-981-10-6171-4_6
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