  Understanding and manipulating deformable objects (e.g., ropes and fabrics)
is an essential yet challenging task with broad applications. Difficulties come
from complex states and dynamics, diverse configurations and high-dimensional
action space of deformable objects. Besides, the manipulation tasks usually
require multiple steps to accomplish, and greedy policies may easily lead to
local optimal states. Existing studies usually tackle this problem using
reinforcement learning or imitating expert demonstrations, with limitations in
modeling complex states or requiring hand-crafted expert policies. In this
paper, we study deformable object manipulation using dense visual affordance,
with generalization towards diverse states, and propose a novel kind of
foresightful dense affordance, which avoids local optima by estimating states'
values for long-term manipulation. We propose a framework for learning this
representation, with novel designs such as multi-stage stable learning and
efficient self-supervised data collection without experts. Experiments
demonstrate the superiority of our proposed foresightful dense affordance.
Project page: https://hyperplane-lab.github.io/DeformableAffordance
