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Title: Generalized Natural Language Grounded Navigation via Environment-agnostic Multitask Learning

Paper content:
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog.
However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments.
In order to close the gap between seen and unseen environments, we aim at learning a generalizable navigation model from two novel perspectives:
(1) we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks;
(2) we propose to learn environment-agnostic representations for navigation policy that are invariant among environments, thus generalizing better on unseen environments.
Extensive experiments show that our environment-agnostic multitask navigation model significantly reduces the performance gap between seen and unseen environments and outperforms the baselines on unseen environments by 16% (relative measure on success rate) on VLN and 120% (goal progress) on NDH, establishing the new state of the art for NDH task.
Navigation in visual environments by following natural language guidance (Hemachandra et al., 2015) is a fundamental capability of intelligent robots that simulate human behaviors, because humans can easily reason about the language guidance and navigate efficiently by interacting with the visual environments.
Recent efforts (Anderson et al., 2018b; Das et al., 2018; Thomason et al., 2019) empower large-scale learning of natural language grounded navigation that is situated in photorealistic simulation environments.
Nevertheless, the generalization problem commonly exists for these tasks, especially indoor navigation: the agent usually performs poorly on unknown environments that have never been seen during training.
One of the main causes for such behavior is data scarcity as it is expensive and time-consuming to extend either visual environments or natural language guidance.
The number of scanned houses for indoor navigation is limited due to high expense and privacy concerns.
Besides, unlike vision-only navigation tasks (Mirowski et al., 2018; Xia et al., 2018; Manolis Savva* et al., 2019; Kolve et al., 2017) where episodes can be exhaustively sampled in simulation, natural language grounded navigation is supported by human demonstrated interaction and communication in natural language.
It is impractical to fully collect and cover all the samples for individual tasks.
Therefore, it is essential though challenging to efficiently learn a more generalized policy for natural language grounded navigation tasks from existing data (Wu et al., 2018a; b) .
In this paper, we study how to resolve the generalization and data scarcity issues from two different angles.
First, previous methods are trained for one task at the time, so each new task requires training a brand new agent instance that can only solve the one task it was trained on.
In this work, we propose a generalized multitask model for natural language grounded navigation tasks such as Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH), aiming at efficiently transferring knowledge across tasks and effectively solving both tasks with one agent simultaneously.
Moreover, although there are thousands of trajectories paired with language guidance, the underlying house scans are restricted.
For instance, the popular Matterport3D dataset (Chang et al., 2017) contains only 61 unique house scans in the training set.
The current models perform much better in seen environments by taking advantage of the knowledge of specific houses they have acquired over multiple task completions during training, but fail to generalize to houses not seen during training.
Hence we propose an environment-agnostic learning method to learn a visual representation that is invariant to specific environments but still able to support navigation.
Endowed with the learned environment-agnostic representations, the agent is further prevented from the overfitting issue and generalizes better on unseen environments.
To the best of our knowledge, we are the first to introduce natural language grounded multitask and environment-agnostic training regimes and validate their effectiveness on VLN and NDH tasks.
Extensive experiments demonstrate that our environment-agnostic multitask navigation model can not only efficiently execute different language guidance in indoor environments but also outperform the single-task baseline models by a large margin on both tasks.
Besides, the performance gap between seen and unseen environments is significantly reduced.
We also set a new state of the art on NDH with over 120% improvement in terms of goal progress.
In this work, we show that the model trained using environment-agnostic multitask learning approach learns a generalized policy for the two natural language grounded navigation tasks.
It closes down the gap between seen and unseen environments, learns more generalized environment representations and effectively transfers knowledge across tasks outperforming baselines on both the tasks simultaneously by a significant margin.
At the same time, the two approaches independently benefit the agent learning and are complementary to each other.
There are possible future extensions to our work-the MT-RCM can further be adapted to other language-grounded navigation datasets, such as those using Street View (e.g., Touchdown (Chen et al., 2019) Table 6 presents a more detailed ablation of Table 5 using different parts of dialog history.
The results prove that agents rewarded for getting closer to the goal room consistently outperform agents rewarded for getting closer to the exact goal location.
Table 7 presents a more detailed analysis from Table 3 with access to different parts of dialog history.
The models with shared language encoder consistently outperform those with separate encoders.
Figure 4: Visualizing performance gap between seen and unseen environments for VLN and NDH tasks.
For VLN, the plotted metric is agent's success rate while for NDH, the metric is agent's progress.

assistant: text: We propose to learn a more generalized policy for natural language grounded navigation tasks via environment-agnostic multitask learning.