Research on Robotics Multi-Modal Perception and Adaptive Control Methods based on Deep Learning

Abstract

With the applications of AI and robotics expanding within service, medical and manufacturing sectors, there has been an increased need to develop robotic systems based on multi-modal perception and adaptive control, to handle complex interactive tasks within dynamic environments. This research presents a multi-modal perception and adaptive control strategy through a unified Transformer structure, an extension to current deep learning frameworks. The model also embeds multi-modal inputs by emotively religious perceptual data of vision, touch and audio through a multi-modal input embedding layer, and effectively fuses and interacts different modal features with self-attention mechanism. In addition, it designs an adaptive control module based on deep reinforcement learning strategy to adjust the control parameters, making the model adaptive to different environment changes and task requirements. Comparison to existing approaches shows that this work provides a simplified and integrated model structure, improving the collaborative efficiency of perception and control. Experimental results show that the proposed model improves perception accuracy and control stability for both the simulated and real robot platform test cases. Compared to the common mainstream methods, the suggested model shows high-level running efficiency and versatility.