  To perform manipulation tasks in the real world, robots need to operate on
objects with various shapes, sizes and without access to geometric models. It
is often unfeasible to train monolithic neural network policies across such
large variance in object properties. Towards this generalization challenge, we
propose to learn modular task policies which compose object-centric task-axes
controllers. These task-axes controllers are parameterized by properties
associated with underlying objects in the scene. We infer these controller
parameters directly from visual input using multi-view dense correspondence
learning. Our overall approach provides a simple, modular and yet powerful
framework for learning manipulation tasks. We empirically evaluate our approach
on multiple different manipulation tasks and show its ability to generalize to
large variance in object size, shape and geometry.
