  In this paper, we propose composable part-based manipulation (CPM), a novel
approach that leverages object-part decomposition and part-part correspondences
to improve learning and generalization of robotic manipulation skills. By
considering the functional correspondences between object parts, we
conceptualize functional actions, such as pouring and constrained placing, as
combinations of different correspondence constraints. CPM comprises a
collection of composable diffusion models, where each model captures a
different inter-object correspondence. These diffusion models can generate
parameters for manipulation skills based on the specific object parts.
Leveraging part-based correspondences coupled with the task decomposition into
distinct constraints enables strong generalization to novel objects and object
categories. We validate our approach in both simulated and real-world
scenarios, demonstrating its effectiveness in achieving robust and generalized
manipulation capabilities.
