Object tactile character recognition model based on attention mechanism LSTM

摘要

Aiming at the problem of lowering the success rate of grasping due to the different characteristics of the grasped object, such as quality, soft and hard, in the process of robotic garbage sorting, this paper proposes an LSTM tactile character recognition model based on the attention mechanism. The model is trained using five tactile capture data sets for tactile traits, and the recognition rate of the object's hardness and hardness traits can reach more than 90%. Experimental results show that the model can effectively identify tactile traits with a high recognition rate. Has good generalization ability.