  Effective planning of long-horizon deformable object manipulation requires
suitable abstractions at both the spatial and temporal levels. Previous methods
typically either focus on short-horizon tasks or make strong assumptions that
full-state information is available, which prevents their use on deformable
objects. In this paper, we propose PlAnning with Spatial-Temporal Abstraction
(PASTA), which incorporates both spatial abstraction (reasoning about objects
and their relations to each other) and temporal abstraction (reasoning over
skills instead of low-level actions). Our framework maps high-dimension 3D
observations such as point clouds into a set of latent vectors and plans over
skill sequences on top of the latent set representation. We show that our
method can effectively perform challenging sequential deformable object
manipulation tasks in the real world, which require combining multiple tool-use
skills such as cutting with a knife, pushing with a pusher, and spreading the
dough with a roller.
