A Transformer Model for Embodied, Language-guided Visual Task Completion
EmBERT
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.
In this repository, we provide the entire codebase which is used for training and evaluating EmBERT performance on the ALFRED dataset. It’s mostly based on AllenNLP and PyTorch-Lightning therefore it’s inherently easily to extend.
Setup
We used