Social interaction between a robot and rats is important since the robot can generate reproducible social behaviors across trials. However, lacking internal state feedback from the rat makes current robot-rat interaction a very preliminary level comparing with rat-rat interaction. Previous biological studies showed that ultrasonic vocalizations (USVs) emitted by a rat are expressions of its internal emotional states, which therefore can be used as part of feedback for a robot-rat interaction. The challenge is to accurately identify rat USVs in real-time from mix sounds generated by the robot in a noisy environment. To address these problems, we propose an SVM-based rat USVs identification method. This SVM method uses three types of features to represents the characteristics of mix sound and use these multidimensional features to identify rat USVs. Results show that our identification method has an accuracy of 84.29% with only 4.84% false-positive rate. Furthermore, we carefully design the filter window length with respect to sound chunk length and use only one microphone to record the mix sound. All of these efforts are to reduce the calculation time to realize real-time identification. Eventually, the identification process can be executed within 3. 5ms, which definitely meet the real-time demand. This research lays the foundation of the feedback based interaction between rat and robot, and also shows promise in the study of ethology and the interaction between robot and animals.