Robots should ideally perceive objects using human-like multi-modal sensing such as vision, tactile feedback, smell, and hearing. However, the features presentations are different for each modal sensor. Moreover,the extracted feature methods for each modal are not the same. Some modal features such as vision, which presents a spatial property, are static while features such as tactile feedback, which presents temporal pattern,are dynamic. It is difficult to fuse these data at the feature level for robot perception. In this study, we propose a framework for the fusion of visual and tactile modal features, which includes the extraction of features, feature vector normalization and generation based on bag-of-system(BoS), and coding by robust multi-modal joint sparse representation(RM-JSR) and classification, thereby enabling robot perception to solve the problem of diverse modal data fusion at the feature level. Finally, comparative experiments are carried out to demonstrate the performance of this framework. Robots should ideally perceive objects using human-like multi-modal sensing such as vision, tactile feedback, smell, and hearing. However, the features presentations are different for each modal sensor. . Some modal features such as vision, which presents a spatial property, are static while features such as tactile feedback, which presents temporal pattern, are dynamic. It is difficult to fuse these data at the feature level for robot perception. In this study, we propose a framework for the fusion of visual and tactile modal features, which includes the extraction of features, feature vector normalization and generation based on bag-of-system (BoS), and coding by robust multi-modal joint sparse representation (RM- JSR) and classification, thereby enabling robot perception to solve the problem of diverse modal data fusion at the feature level. Finally, comparative experiments are carried out to to the performan ce of this framework.