  End-to-end Transformers have demonstrated an impressive success rate for
Embodied Instruction Following when the environment has been seen in training.
However, they tend to struggle when deployed in an unseen environment. This
lack of generalizability is due to the agent's insensitivity to subtle changes
in natural language instructions. To mitigate this issue, we propose explicitly
aligning the agent's hidden states with the instructions via contrastive
learning. Nevertheless, the semantic gap between high-level language
instructions and the agent's low-level action space remains an obstacle.
Therefore, we further introduce a novel concept of meta-actions to bridge the
gap. Meta-actions are ubiquitous action patterns that can be parsed from the
original action sequence. These patterns represent higher-level semantics that
are intuitively aligned closer to the instructions. When meta-actions are
applied as additional training signals, the agent generalizes better to unseen
environments. Compared to a strong multi-modal Transformer baseline, we achieve
a significant 4.5% absolute gain in success rate in unseen environments of
ALFRED Embodied Instruction Following. Additional analysis shows that the
contrastive objective and meta-actions are complementary in achieving the best
results, and the resulting agent better aligns its states with corresponding
instructions, making it more suitable for real-world embodied agents. The code
is available at: https://github.com/joeyy5588/LACMA.
