  Perception is an essential part of robotic manipulation in a semi-structured
environment. Traditional approaches produce a narrow task-specific prediction
(e.g., object's 6D pose), that cannot be adapted to other tasks and is
ill-suited for deformable objects. In this paper, we propose using canonical
mapping as a near-universal and flexible object descriptor. We demonstrate that
common object representations can be derived from a single pre-trained
canonical mapping model, which in turn can be generated with minimal manual
effort using an automated data generation and training pipeline. We perform a
multi-stage experiment using two robot arms that demonstrate the robustness of
the perception approach and the ways it can inform the manipulation strategy,
thus serving as a powerful foundation for general-purpose robotic manipulation.
