  One of the challenges of full autonomy is to have a robot capable of
manipulating its current environment to achieve another environment
configuration. This paper is a step towards this challenge, focusing on the
visual understanding of the task. Our approach trains a deep neural network to
represent images as measurable features that are useful to estimate the
progress (or phase) of a task. The training uses numerous variations of images
of identical tasks when taken under the same phase index. The goal is to make
the network sensitive to differences in task progress but insensitive to the
appearance of the images. To this end, our method builds upon Time-Contrastive
Networks (TCNs) to train a network using only discrete snapshots taken at
different stages of a task. A robot can then solve long-horizon tasks by using
the trained network to identify the progress of the current task and by
iteratively calling a motion planner until the task is solved. We quantify the
granularity achieved by the network in two simulated environments. In the
first, to detect the number of objects in a scene and in the second to measure
the volume of particles in a cup. Our experiments leverage this granularity to
make a mobile robot move a desired number of objects into a storage area and to
control the amount of pouring in a cup.
