  Seemingly simple natural language requests to a robot are generally
underspecified, for example "Can you bring me the wireless mouse?" Flat images
of candidate mice may not provide the discriminative information needed for
"wireless." The world, and objects in it, are not flat images but complex 3D
shapes. If a human requests an object based on any of its basic properties,
such as color, shape, or texture, robots should perform the necessary
exploration to accomplish the task. In particular, while substantial effort and
progress has been made on understanding explicitly visual attributes like color
and category, comparatively little progress has been made on understanding
language about shapes and contours. In this work, we introduce a novel
reasoning task that targets both visual and non-visual language about 3D
objects. Our new benchmark, ShapeNet Annotated with Referring Expressions
(SNARE) requires a model to choose which of two objects is being referenced by
a natural language description. We introduce several CLIP-based models for
distinguishing objects and demonstrate that while recent advances in jointly
modeling vision and language are useful for robotic language understanding, it
is still the case that these image-based models are weaker at understanding the
3D nature of objects -- properties which play a key role in manipulation. We
find that adding view estimation to language grounding models improves accuracy
on both SNARE and when identifying objects referred to in language on a robot
platform, but note that a large gap remains between these models and human
performance.
