  We present a novel approach to weakly supervised object detection. Instead of
annotated images, our method only requires two short videos to learn to detect
a new object: 1) a video of a moving object and 2) one or more "negative"
videos of the scene without the object. The key idea of our algorithm is to
train the object detector to produce physically plausible object motion when
applied to the first video and to not detect anything in the second video. With
this approach, our method learns to locate objects without any object location
annotations. Once the model is trained, it performs object detection on single
images. We evaluate our method in three robotics settings that afford learning
objects from motion: observing moving objects, watching demonstrations of
object manipulation, and physically interacting with objects (see a video
summary at https://youtu.be/BH0Hv3zZG_4).
