  A grand goal in AI is to build a robot that can accurately navigate based on
natural language instructions, which requires the agent to perceive the scene,
understand and ground language, and act in the real-world environment. One key
challenge here is to learn to navigate in new environments that are unseen
during training. Most of the existing approaches perform dramatically worse in
unseen environments as compared to seen ones. In this paper, we present a
generalizable navigational agent. Our agent is trained in two stages. The first
stage is training via mixed imitation and reinforcement learning, combining the
benefits from both off-policy and on-policy optimization. The second stage is
fine-tuning via newly-introduced 'unseen' triplets (environment, path,
instruction). To generate these unseen triplets, we propose a simple but
effective 'environmental dropout' method to mimic unseen environments, which
overcomes the problem of limited seen environment variability. Next, we apply
semi-supervised learning (via back-translation) on these dropped-out
environments to generate new paths and instructions. Empirically, we show that
our agent is substantially better at generalizability when fine-tuned with
these triplets, outperforming the state-of-art approaches by a large margin on
the private unseen test set of the Room-to-Room task, and achieving the top
rank on the leaderboard.
