Robotic systems employed in tasks such as navigation, target tracking, security, and surveillance often use camera gimbal systems to enhance their monitoring and security capabilities. These camera gimbal systems undergo fast to-and-fro rotational motion to surveil the extended field of view (FOV). A high steering rate (rotation angle per second) of the gimbal is essential to revisit a given scene as fast as possible, which results in significant motion blur in the captured video frames. Real-time motion deblurring is essential in surveillance robots since the subsequent image-processing tasks demand immediate availability of blur-free images. Existing deep learning (DL) based motion deblurring methods either lack real-time performance due to network complexity or suffer from poor deblurring quality for large motion blurs. In this work, we propose a Gyro-guided Network for Real-time motion deblurring (GRNet) which makes effective use of existing prior information to improve deblurring without increasing the complexity of the network. The steering rate of the gimbal is taken as a prior for data generation. A contrastive learning scheme is introduced for the network to learn the amount of blur in an image by utilizing the knowledge of blur content in images during training. To the GRNet, a sharp reference image is additionally given as input to guide the deblurring process. The most relevant features from the reference image are selected using a cross-attention module. Our method works in real-time at 30 fps. As a first, we propose a Gimbal Yaw motion Real-wOrld (GYRO) dataset of infrared (IR) as well as color images with significant motion blur along with the inertial measurements of camera rotation, captured by a gimbal-based imaging setup where the gimbal undergoes rotational yaw motion. Both qualitative and quantitative evaluations on our proposed GYRO dataset, demonstrate the practical utility of our method.