A key challenge in robotics is the capability to perceive unseen objects, which can improve a robot’s ability to learn from and adapt to its surroundings. One approach is to employ unsupervised, salient object discovery methods, which have shown promise in the computer vision literature. However, most state-of-the-art methods are unsuitable for robotics because they are limited to processing whole video segments before discovering objects, which can constrain real-time perception. To address these gaps, we introduce Unsupervised Foraging of Objects (UFO), a novel, unsupervised, salient object discovery method designed for monocular robot vision. We designed UFO with a parallel discover-prediction paradigm, permitting it to discover arbitrary, salient objects on a frame-by-frame basis, which can help robots to engage in scalable object learning. We compared UFO to the two fastest and most accurate methods for unsupervised salient object discovery (Fast Segmentation and Saliency-Aware Geodesic), and show that UFO 6.5 times faster, achieving state-of-the-art precision, recall, and accuracy. Furthermore our evaluation suggests that UFO is robust to real- world perception challenges encountered by robots, including moving cameras and moving objects, motion blur, and occlusion. It is our goal that this work will be used with other robot perception methods, to design robots that can learn novel object concepts, leading to improved autonomy.