The Biomechanics of the Tactile Perception of Friction

Willemet, Laurence

doi:10.1007/978-3-031-16053-0

Cited by 1 publication

(1 citation statement)

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“…On the other hand, the tactile sensor can recognize and provide physical property information of the target. Tactile feedback helps the manipulator to perceive the hardness [5], roughness [6], [7], and friction [8] of the target while grasping it, and adding tactile sensors to the manipulator can help to obtain tactile perception, which assists in estimating the stability of the target grasp and target recognition. Therefore, developing a method for robots to learn human tactile perception is an urgent scientific problem in the field of intelligent robot research.…”

Section: Introductionmentioning

confidence: 99%

Tactile-GAT: Tactile Graph Attention Networks for Robot Tactile Perception Classification

Chen,

Zhu

2023

Preprint

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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 make good use of 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 information between different tactile signal channels. We constructed a tactile glove and collected a dataset of pressure and bending tactile signals during grasping and holding objects. And our method achieved 89.58% accuracy in object tactile signal classification with a small parameter size of 0.11M. Compared to existing time-series signal classification algorithms, our graph-based tactile perception algorithm can utilize and learn sensor spatial information, making it more suitable for processing multi-channel tactile data. Our method can serve as a general strategy to improve robot's tactile perception capabilities.

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