  Contrary to the vast literature in modeling, perceiving, and understanding
agent-object (e.g., human-object, hand-object, robot-object) interaction in
computer vision and robotics, very few past works have studied the task of
object-object interaction, which also plays an important role in robotic
manipulation and planning tasks. There is a rich space of object-object
interaction scenarios in our daily life, such as placing an object on a messy
tabletop, fitting an object inside a drawer, pushing an object using a tool,
etc. In this paper, we propose a unified affordance learning framework to learn
object-object interaction for various tasks. By constructing four object-object
interaction task environments using physical simulation (SAPIEN) and thousands
of ShapeNet models with rich geometric diversity, we are able to conduct
large-scale object-object affordance learning without the need for human
annotations or demonstrations. At the core of technical contribution, we
propose an object-kernel point convolution network to reason about detailed
interaction between two objects. Experiments on large-scale synthetic data and
real-world data prove the effectiveness of the proposed approach. Please refer
to the project webpage for code, data, video, and more materials:
https://cs.stanford.edu/~kaichun/o2oafford
