The article proposes an approach to improve the interaction between humans and a collaborative robotic system (CRS) based on multimodal emotion recognition. Modules for speech, text, video and voice analysis are presented, as well as methods for aggregating the results of various modalities. The developed architecture of the multimodal system allows you to adapt to changes in the emotional state of the operator in real time. During the experiments, the proposed algorithm was evaluated using real industrial data. Special attention is paid to the training of models, accuracy metrics and their integration into the overall architecture of the collaborative robotic systems. The results of the study demonstrate the prospects of using multimodal approaches in industrial robotics. Experiments have shown that the proposed approach demonstrates high accuracy in recognizing emotions, surpassing traditional methods. The results obtained confirm the possibility of using the developed system in collaborative robotic systems to improve adaptability and interaction with operators in dynamic production conditions. Further development of the method will be aimed at increasing the system’s resistance to noise and various data variations, as well as expanding its application in other areas.