Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments.
This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal.
In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress.
We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components.
Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set).
Code is available at https://github.com/chihyaoma/selfmonitoring-agent.
Recently, the Vision-and-Language (VLN) navigation task BID3 , which requires the agent to follow natural language instructions to navigate through a photo-realistic unknown environment, has received significant attention BID46 BID19 ).
In the VLN task, an agent is placed in an unknown realistic environment and is required to follow natural language instructions to navigate from its starting location to a target location.
In contrast to some existing navigation tasks BID24 BID53 BID33 , we address the class of tasks where the agent does not have an explicit representation of the target (e.g., location in a map or image representation of the goal) to know if the goal has been reached or not BID31 BID22 BID18 BID6 .
Instead, the agent needs to be aware of its navigation status through the association between the sequence of observed visual inputs to instructions.Consider an example as shown in FIG0 , given the instruction "Exit the bedroom and go towards the table. Go to the stairs on the left of the couch. Wait on the third step.", the agent first needs to locate which instruction is needed for the next movement, which in turn requires the agent to be aware of (i.e., to explicitly represent or have an attentional focus on) which instructions were completed or ongoing in the previous steps.
For instance, the action "Go to the stairs" should be carried out once the agent has exited the room and moved towards the table.
However, there exists inherent ambiguity for "go towards the table".
Intuitively, the agent is expected to "Go to the stairs" after completing "go towards the table".
But, it is not clear what defines the completeness of "Go towards the table".
The completeness of an ongoing action often depends on the availability of the next action.
Since the transition between past and next part of the instructions is a soft boundary, in order to determine when to transit and to follow the instruction correctly the agent is required to keep track of both grounded instructions.
On the other hand, assessing the progress made towards the goal has indeed been shown to be important for goal-directed tasks in humans decision-making BID8 BID12 BID9 .
While a number of approaches have been proposed for VLN BID3 BID46 BID19 , previous approaches generally are not aware of which instruction is next nor progress towards the goal; indeed, we qualitatively show that even the attentional mechanism of the baseline does not successfully track this information through time.In this paper, we propose an agent endowed with the following abilities: (1) identify which direction to go by finding the part of the instruction that corresponds to the observed images-visual grounding, (2) identify which part of the instruction has been completed or ongoing and which part is potentially needed for the next action selection-textual grounding, and (3) ensure that the grounded instruction can correctly be used to estimate the progress made towards the goal, and apply regularization to ensure this -progress monitoring.
Therefore, we introduce the self-monitoring agent consisting of two complementary modules: visual-textual co-grounding and progress monitor.More specifically, we achieve both visual and textual grounding simultaneously by incorporating the full history of grounded instruction, observed images, and selected actions into the agent.
We leverage the structural bias between the words in instructions used for action selection and progress made towards the goal and propose a new objective function for the agent to measure how well it can estimate the completeness of instruction-following.
We then demonstrate that by conditioning on the positions and weights of grounded instruction as input, the agent can be self-monitoring of its progress and further ensure that the textual grounding accurately reflects the progress made.Overall, we propose a novel self-monitoring agent for VLN and make the following contributions: (1) We introduce the visual-textual co-grounding module, which performs grounding interdependently across both visual and textual modalities.
We show that it can outperform the baseline method by a large margin.
(2) We propose to equip the self-monitoring agent with a progress monitor, and for navigation tasks involving instructions instantiate this by introducing a new objective function for training.
We demonstrate that, unlike the baseline method, the position of grounded instruction can follow both past and future instructions, thereby tracking progress to the goal.
(3) With the proposed self-monitoring agent, we set the new state-of-the-art performance on both seen and unseen environments on the standard benchmark.
With 8% absolute improvement in success rate on the unseen test set, we are ranked #1 on the challenge leaderboard.
We introduce a self-monitoring agent which consists of two complementary modules: visual-textual co-grounding module and progress monitor.
The visual-textual co-grounding module locates the instruction completed in the past, the instruction needed in the next action, and the moving direction from surrounding images.
The progress monitor regularizes and ensures the grounded instruction correctly reflects the progress towards the goal by explicitly estimating the completeness of instruction-following.
This estimation is conditioned on the positions and weights of grounded instruction.
Our approach sets a new state-of-the-art performance on the standard Room-to-Room dataset on both seen and unseen environments.
While we present one instantiation of self-monitoring for a decision-making agent, we believe that this concept can be applied to other domains as well.
BID46 , and Speaker-Follower BID19 .
*: with data augmentation.
TAB4 .
We can see that our proposed method outperformed existing approaches with a large margin on both validation unseen and test sets.
Our method with greedy decoding for action selection improved the SR by 9% and 8% on validation unseen and test set.
When using progress inference for action selection, the performance on the test set significantly improved by 5% compared to using greedy decoding, yielding 13% improvement over the best existing approach.
Network architecture.
The embedding dimension for encoding the navigation instruction is 256.
We use a dropout layer with ratio 0.5 after the embedding layer.
We then encode the instruction using a regular LSTM, and the hidden state is 512 dimensional.
The MLP g used for projecting the raw image feature is BN − → F C − → BN − → Dropout − → ReLU .
The FC layer projects the 2176-d input vector to a 1024-d vector, and the dropout ratio is set to be 0.5.
The hidden state of the LSTM used for carrying the textual and visual information through time in Eq. 1 is 512.
We set the maximum length of instruction to be 80, thus the dimension of the attention weights of textual grounding α t is also 80.
The dimension of the learnable matrices from Eq. 2 to 5 are: DISPLAYFORM0 DISPLAYFORM1 closest previous trajectory, so that when a single agent traverses through all recorded trajectories, the overhead for switching from one trajectory to another can be reduced significantly.
The final selected trajectory from beam search is then lastly logged to the trajectory.
This therefore yields exactly the same success rate and navigation error, as the metrics are computed according to the last viewpoint from a trajectory.
We propose a self-monitoring agent for the Vision-and-Language Navigation task.