  Despite great strides in language-guided manipulation, existing work has been
constrained to table-top settings. Table-tops allow for perfect and consistent
camera angles, properties are that do not hold in mobile manipulation. Task
plans that involve moving around the environment must be robust to egocentric
views and changes in the plane and angle of grasp. A further challenge is
ensuring this is all true while still being able to learn skills efficiently
from limited data. We propose Spatial-Language Attention Policies (SLAP) as a
solution. SLAP uses three-dimensional tokens as the input representation to
train a single multi-task, language-conditioned action prediction policy. Our
method shows an 80% success rate in the real world across eight tasks with a
single model, and a 47.5% success rate when unseen clutter and unseen object
configurations are introduced, even with only a handful of examples per task.
This represents an improvement of 30% over prior work (20% given unseen
distractors and configurations). We see a 4x improvement over baseline in
mobile manipulation setting. In addition, we show how SLAPs robustness allows
us to execute Task Plans from open-vocabulary instructions using a large
language model for multi-step mobile manipulation. For videos, see the website:
https://robotslap.github.io
