  Language-guided robots performing home and office tasks must navigate in and
interact with the world. Grounding language instructions against visual
observations and actions to take in an environment is an open challenge. We
present Embodied BERT (EmBERT), a transformer-based model which can attend to
high-dimensional, multi-modal inputs across long temporal horizons for
language-conditioned task completion. Additionally, we bridge the gap between
successful object-centric navigation models used for non-interactive agents and
the language-guided visual task completion benchmark, ALFRED, by introducing
object navigation targets for EmBERT training. We achieve competitive
performance on the ALFRED benchmark, and EmBERT marks the first
transformer-based model to successfully handle the long-horizon, dense,
multi-modal histories of ALFRED, and the first ALFRED model to utilize
object-centric navigation targets.
