  We focus on the task of language-conditioned object placement, in which a
robot should generate placements that satisfy all the spatial relational
constraints in language instructions. Previous works based on rule-based
language parsing or scene-centric visual representation have restrictions on
the form of instructions and reference objects or require large amounts of
training data. We propose an object-centric framework that leverages foundation
models to ground the reference objects and spatial relations for placement,
which is more sample efficient and generalizable. Experiments indicate that our
model can achieve a 97.75% success rate of placement with only ~0.26M trainable
parameters. Besides, our method generalizes better to both unseen objects and
instructions. Moreover, with only 25% training data, we still outperform the
top competing approach.
