  Relative placement tasks are an important category of tasks in which one
object needs to be placed in a desired pose relative to another object.
Previous work has shown success in learning relative placement tasks from just
a small number of demonstrations when using relational reasoning networks with
geometric inductive biases. However, such methods cannot flexibly represent
multimodal tasks, like a mug hanging on any of n racks. We propose a method
that incorporates additional properties that enable learning multimodal
relative placement solutions, while retaining the provably
translation-invariant and relational properties of prior work. We show that our
method is able to learn precise relative placement tasks with only 10-20
multimodal demonstrations with no human annotations across a diverse set of
objects within a category.
