Semantic Communication and Predictive Compression of Kinaesthetic Signals in Robotics With Learnable Matrices

ABSTRACT Robotics plays an increasingly important role in all areas of human activity. Teleoperation robots can effectively ensure the safety of operators when operating in difficult and high‐risk industrial scenarios, which obviously requires instant and efficient signal compression and transmission in the system. However, most of the existing algorithms cannot fully explore the correlation within the signal, which mostly limits the compression efficiency. In this paper, a novel prediction‐aided kinaesthetic‐signal compression framework is proposed, which uses semantic communication methods to explore the temporal and spatial correlations of signals and employs neural network predictions to uncover their internal correlations. Specifically, the signal is first divided into two groups: the base part and the predictable part, and then a series of transformation matrices are introduced to establish the correlation between the two groups of the signal, which can be automatically optimised by a well‐designed neural network. This strategy of using learnable transformation matrices for prediction can not only accurately construct the correlation within the signal through massive data mining but also efficiently execute inference in a simple matrix multiplication computing form. Experimental results demonstrate that the proposed method outperforms the existing traditional tactile codecs and the latest tactile semantic communication methods.