  A core capability for robot manipulation is reasoning over where and how to
stably place objects in cluttered environments. Traditionally, robots have
relied on object-specific, hand-crafted heuristics in order to perform such
reasoning, with limited generalizability beyond a small number of object
instances and object interaction patterns. Recent approaches instead learn
notions of physical interaction, namely motion prediction, but require
supervision in the form of labeled object information or come at the cost of
high sample complexity, and do not directly reason over stability or object
placement. We present 6-DoFusion, a generative model capable of generating 3D
poses of an object that produces a stable configuration of a given scene.
Underlying 6-DoFusion is a diffusion model that incrementally refines a
randomly initialized SE(3) pose to generate a sample from a learned,
context-dependent distribution over stable poses. We evaluate our model on
different object placement and stacking tasks, demonstrating its ability to
construct stable scenes that involve novel object classes as well as to improve
the accuracy of state-of-the-art 3D pose estimation methods.
