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Language-guided robots performing home and office tasks must navigate in andinteract with the world. Grounding language instructions against visualobservations and actions to take in an environment is an open challenge. Wepresent Embodied BERT (EmBERT), a transformer-based model which can attend tohigh-dimensional, multi-modal inputs across long temporal horizons forlanguage-conditioned task completion. Additionally, we bridge the gap betweensuccessful object-centric navigation models used for non-interactive agents andthe language-guided visual task completion benchmark, ALFRED, by introducingobject navigation targets for EmBERT training. We achieve competitiveperformance on the ALFRED benchmark, and EmBERT marks the firsttransformer-based model to successfully handle the long-horizon, dense,multi-modal histories of ALFRED, and the first ALFRED model to utilizeobject-centric navigation targets.

Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion