  Two main approaches have been developed for learning first-order planning
(action) models from unstructured data: combinatorial approaches that yield
crisp action schemas from the structure of the state space, and deep learning
approaches that produce action schemas from states represented by images. A
benefit of the former approach is that the learned action schemas are similar
to those that can be written by hand; a benefit of the latter is that the
learned representations (predicates) are grounded on the images, and as a
result, new instances can be given in terms of images. In this work, we develop
a new formulation for learning crisp first-order planning models that are
grounded on parsed images, a step to combine the benefits of the two
approaches. Parsed images are assumed to be given in a simple O2D language
(objects in 2D) that involves a small number of unary and binary predicates
like "left", "above", "shape", etc. After learning, new planning instances can
be given in terms of pairs of parsed images, one for the initial situation and
the other for the goal. Learning and planning experiments are reported for
several domains including Blocks, Sokoban, IPC Grid, and Hanoi.
