Tactile-GAT: tactile graph attention networks for robot tactile perception classification.

ABSTRACT: As one of the most important senses in human beings, touch can also help robots better perceive and adapt to complex environmental information, improving their autonomous decision-making and execution capabilities. Compared to other perception methods, tactile perception needs to handle multi-channel tactile signals simultaneously, such as pressure, bending, temperature, and humidity. However, directly transferring deep learning algorithms that work well on temporal signals to tactile signal tasks does not effectively utilize the physical spatial connectivity information of tactile sensors. In this paper, we propose a tactile perception framework based on graph attention networks, which incorporates explicit and latent relation graphs. This framework can effectively utilize the structural

SUBMITTER: Chen L 

PROVIDER: S-EPMC11555220 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature