Dialog-enabled smart assistants, which communicate via natural language and occupy human homes, have seen widespread adoption in recent years. These systems can communicate information, but do not manipulate objects or actuate. By contrast, manipulation-capable and mobile robots are still largely deployed in industrial settings, but do not interact with human users. Dialog-enabled robots can bridge this gap, with natural language interfaces helping robots and non-experts collaborate to achieve their goals [1, 2, 3, 4, 5].
Navigating successfully from place to place is a fundamental need for a robot in a human environment and can be facilitated, as with smart assistants, through dialog. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation (CVDN), an English language dataset situated in the Matterport Room-2-Room (R2R) simulation environment [6, 7] (Figure 1). CVDN can be used to train navigation agents, such as language teleoperated home and office robots, that ask targeted questions about where to go next when unsure. Additionally, CVDN can be used to train agents that can answer such questions given expert knowledge of the environment to enable automated language guidance for humans in unfamiliar places (e.g., asking for directions in an office building). The photorealistic environment used in CVDN may enable agents trained in simulation to conduct and understand dialog from humans to transfer those skills to the real world. The dialogs in CVDN contain nearly three times as many words as R2R instructions, and cover average path lengths more than three times longer than paths in R2R.
In Section 2 we situate the Vision-and-Dialog Navigation paradigm. After introducing CVDN (Section 3), we create the Navigation from Dialog History (NDH) task with over 7k instances from CVDN dialogs (Section 4). We evaluate an initial, sequence-to-sequence model on this task (Section 5). The sequence-to-sequence model encodes the human-human dialog so far and uses it to infer navigation actions to get closer to a goal location. We find that agents perform better with more dialog history and when mixing human and planner supervision during training. We conclude with next directions for creating tasks from CVDN, such as two learning agents that must be trained cooperatively, and more nuanced models for NDH, where our initial sequence-to-sequence model leaves headroom between its performance and human-level performance (Section 6).
2 Related Work and Background
Dialogs in CVDN begin with an underspecified, ambiguous instruction analogous to what robots may encounter in a home environment (e.g., “Go to the room with the bed”). Dialogs include both navigation and question asking / answering to guide the search, akin to a robot agent asking for clarification when moving through a new environment. Table 1 summarizes how CVDN combines the strengths and difficulties of a subset of existing navigation and question answering tasks.
Early, simulator-based Vision-and-Language Navigation (VLN) tasks use language instructions that are unambiguous—designed to uniquely describe the goal—and fully specified—describing the steps necessary to reach the goal [8, 9]. In a more recent setting, a simulated quadcopter drone uses low-level controls to follow a route described in natural language . In photorealistic simulation environments, agents can navigate high-definition scans of indoor scenes  or large, outdoor city spaces . In interactive question answering [12, 13] settings, the language context is a single question (e.g., “What color is the car?”) that requires navigation to answer. The questions serve as underspecified instructions, but are unambiguous (e.g., there is only one car whose color can be asked about). These questions are generated from templates rather than human language. In CVDN, input is an underspecified hint about the goal location (e.g., “The goal room has a sink”) requiring exploration and dialog to resolve. Rather than single instructions, CVDN uses two-sided, human-human dialogs.
Question Answering and Dialog.
In Visual Question Answering (VQA), agents answer language questions about a static image. These tasks exist for templated language on rendered images  and human language on real-world images [15, 16, 17]. Later extensions feature two-sided dialog, where a series of question-answer pairs provide context for the next question [18, 19]
. Question answering in natural language processing is a long-studied task for questions about static text documents (e.g., the Stanford QA Dataset). Recently, this paradigm was extended to two-sided dialogs via human-human, question-answer pairs about a document [21, 22, 23]. Questions in these datasets are unambiguous: they have a right answer that can be inferred from the context. By contrast, CVDN conversations begin with a hint about the goal location that is always ambiguous and requires cooperation between participants. Contrasting VQA, because CVDN extends navigation the visual context is temporally dynamic—new visual observations arrive at each timestep.
|Dataset||—Language Context—||—Visual Context—|
|MARCO[8, 9], DRIF||✓||✗||✗||1I||✗||Dynamic||-|
|VQA[15, 16, 17]||✓||✗||-||1Q||✓||Static||-|
In human-robot collaboration, language requests for human help can be generated by robot agents, with human help being non-verbal (e.g, moving a table leg to be within reach for the robot) . However, humans may use language to respond to robot requests for help in task-oriented dialogs [3, 5, 26]. Recent work adds requesting navigation help as an action, but the response comes in the form of templated language that encodes gold-standard planner action sequences . Past work introduced Talk the Walk (TtW) , where two humans communicate to reach a goal location in a photorealistic, outdoor environment. In TtW, the guiding human does not have an egocentric view of the environment, but an abstracted semantic map, and so language grounding centers around semantic elements like “bank” and “restaurant” rather than visual features, and the target location is unambiguously shown to the guide from the start. In CVDN, a Navigator human generates language requests for help, and an Oracle human answers in language conditioned on higher-level, visual observations of what a shortest-path planner would do next, with both players observing the same, egocentric visual context. In some ways, CVDN echoes an older human-human, spoken dialog corpus of map-based navigation , though that corpus is considerably smaller and has fewer and less rich environments.
Background: Matterport Simulator and the Room-2-Room Task.
We build on the R2R task  and train navigation agents using the same simulator and API. MatterPort contains 90 3D house scans, with each scan divided into visual panoramas (nodes which a navigation agent can occupy) accompanied by an adjacency matrix . We differentiate between the steps and distance between and —steps represent the number of intervening nodes , while distance is defined in meters as . Step distance is the number of hops through to get from node to node . The distance in meters is defined as physical distance if or the shortest route between and otherwise. On average, step corresponds to meters.
At each timestep, an agent emits a navigation action taken in the simulated environment. The actions are to turn left or right, tilt up or down, move forward to an adjacent node, or stop. After taking any action except stop, the agent receives a new visual observation from the environment. The forward action is only available if the agent is facing an adjacent node.
3 The Cooperative Vision-and-Dialog Navigation Dataset
We collect 2050 human-human navigation dialogs, comprising over 7k navigation trajectories punctuated by question-answer exchanges, across 83 MatterPort  houses.111A demonstration video of the data collection interface: https://youtu.be/BonlITv_PKw. We prompt with initial instructions that are both ambiguous and underspecified. An ambiguous navigation instruction is one that requires clarification because it can refer to more than one possible goal location. An underspecified navigation instruction is one that does not describe the route to the goal.
A dialog prompt is a tuple of the house scan , a target object to be found, a starting position , and a goal region .
We use the MatterPort object segmentations to get region locations for household objects, as in prior work .
We define a set of 81 unique object types that appear in at least 5 unique houses and appear between 2 and 4 times per such house.222 We also cut odd (“soffet”) and non-specific (“wall”) objects, and merge similar object names (e.g., “potted plant” and “plant”) to cut down the initial 929 object types to these salient 81. Some houses do not have objects that meet our criteria, so CVDN represents only 83 of the 90 total MatterPort houses.
We also cut odd (“soffet”) and non-specific (“wall”) objects, and merge similar object names (e.g., “potted plant” and “plant”) to cut down the initial 929 object types to these salient 81. Some houses do not have objects that meet our criteria, so CVDN represents only 83 of the 90 total MatterPort houses.Each dialog begins with a hint, such as “The goal room contains a plant,” which by construction is both ambiguous (there are two to four rooms with a plant) and underspecified (the path to the room is not described by the hint).
Given a house scan and a target object , a dialog prompt is created for every goal region in the house containing an instance of . Goal regions are sets of nodes that occupy the same room in a house scan. The starting node is chosen to maximize the distance between and the goal regions containing . Formally,
Crowdsourced Data Collection.
We gathered human-human dialogs through Amazon Mechanical Turk.333https://cvdn.dev/. Connect with two tabs to start a dialog with yourself. In each Human Intelligence Task (HIT), workers read about the roles of Navigator and Oracle and could practice using the navigation interface. Pairs of workers were connected to one another via a chat interface.
Every dialog was instantiated via a randomly chosen prompt , with the Navigator starting at panorama and both workers instructed via the text: “Hint: The goal room contains a .” The dialog begins with the Navigator’s turn. On the Navigator’s turn, they could navigate, type a natural language question to ask the Oracle, or guess that they had found the goal room. Incorrect guesses disabled further navigation and forced the Navigator to ask a question to the Oracle. Throughout navigation, the Oracle was shown the steps being taken as a mirror of the Navigator’s interface, so that both workers were always aware of the current visual frame. On the Oracle’s turn, they could view an animation depicting the next hops through the navigation graph towards the goal room according to a shortest path planner and communicate back to the Navigator via natural language (Figure 1). Five hops was chosen because this is slightly shorter than the hop average path in the R2R dataset, for which human annotators were able to provide reasonable language descriptions. Each HIT paid per worker, the entire dataset collection cost over $7k.
After successfully locating the goal room, workers rated their partner’s cooperativeness (from 1 to 5). Workers who failed to maintain a 4 or higher average peer rating were disallowed from taking more of our HITs. On average, dialog participants’ mean peer rating is out of 5 across CVDN.
The CVDN dataset has longer routes and language contexts than the R2R task. The dialogs exhibit complex phenomena that require both dialog and navigation history to resolve.
|Ego||Oracle: Turn slightly to your right and go forward down the hallway|
|Needs Q||-||Navigator: Should I turn left down the hallway ahead?|
|Needs Dialog History||Oracle: Through the lobby. So go through the door next to the green towel. Go to the left door next to the two yellow lights. Walk straight to the end of the hallway and stop|
|Navigator: Are these the yellow lights you were talking about?|
|Needs Nav History||Oracle: You were there briefly but left. There is a turntable behind you a bit. Enter the bedroom next to it.|
|Repair||Oracle: I am so sorry I meant for you to look over to the right not the left|
|Off-topic||Navigator: I am to the ‘rear’ of the zebra. Nice one.|
|Oracle: Ok hold your nose and go to the left of the zebra, through the livingroom and kitchen and towards the bedroom you can see past that|
|Vacuous||Navigator: Ok, now where?|
Figure 2 shows the distributions of path lengths, word counts, and number of utterances across dialogs in the CVDN dataset. Human () and planner (
) path lengths are on average more than three times longer, and have higher variance, than the path lengths in R2R (). Average word counts for navigators () and oracles () sum to an average words per dialog, again exceeding the Room-to-Room average of words per instruction by nearly three times. Dialogs average about 6 utterances each (3 question and answer exchanges), with a fraction being much longer—up to 26 utterances. Some dialogs have no exchanges (about 5%): the Navigator was able to find the goal location by intuition alone given the hint. Because more than one room always contains , these are ‘lucky’ guesses.
We randomly sampled 100 dialogs with at least one QA exchange and annotated whether each utterance (out of 342 per speaker) exhibited certain phenomena (Table 2). Over half the utterances from both Navigator and Oracle roles, and over 90% of all dialogs, contain egocentric references requiring the agent’s position and orientation to interpret. Some Oracle answers require the Navigator question to resolve (e.g., when the answer is just a confirmation). Some utterances need dialog history from previous exchanges or past visual navigation information. More than 10% of dialogs exhibit conversational repair, when speakers try to rectify mistakes. Speakers sometimes establish rapport with off-topic comments and jokes. Both speakers, especially those in the Navigator role, sometimes send vacuous communications, but this is limited to a smaller percentage of dialogs.
Models attempting to perform navigation, ask questions, or answer questions about an embodied environment must grapple with these types of phenomena. For example, an agent may need to attend not just to the last QA exchange, but to the entire dialog and navigation history in order to correctly follow instructions.
4 The Navigation from Dialog History Task
CVDN facilities training agents for navigation, question asking, and question answering. In this paper, we focus on navigation. The ability to navigate successfully given dialog history is key to any future work in the Vision-and-Dialog Navigation paradigm. Every dialog is a sequence of Navigator question and Oracle answer exchanges, with Navigator steps following each exchange. We use this structure to divide dialogs into Navigation from Dialog History (NDH) instances.
In particular, CVDN instances are each comprised of a repeating sequence of navigation actions, , questions asked by the Navigator, , and answers from the Oracle, . Because sending a question or answer ends the worker’s turn, every question and answer is a single string of tokens. For each dialog with prompt , an NDH instance is created for each of . The input is and a (possibly empty) history of questions and answers . The task is to predict navigation actions that bring the agent closer to the goal location , starting from the terminal node of (or , for ). We extract 7415 NDH instances from the 2050 navigation dialogs in CVDN.
We divide these instances into training, validation, and test folds, preserving the R2R folds by house scan. This division is further done by dialog, such that for every dialog in CVDN the NDH instances created from it all belong to the same fold. As in R2R, we split the validation fold into seen and unseen house scans, depending on whether the scan is present in the training set. This results in 4742 training, 382 seen validation, 907 unseen validation, and 1384 unseen test instances.
We provide two forms of supervision for the NDH task: , the navigation steps taken by the Navigator after question-answer exchange , and , the shortest-path steps shown to the Oracle and used as context to provide answer . In each instance of the task, indexes the QA exchange in the dialog from which the instance is drawn (with an empty QA followed by initial navigation steps). Across NDH instances, the steps range in length from 1 to 40 (average ), and the steps range in length from 0 to 5 (average ). The Navigator often continues farther than what the Oracle describes, using their intuition about the house layout to seek the target object.
We evaluate performance on this task by measuring how much progress the agent makes towards . Let be the end node of path , the beginning, and the path inferred by the navigation agent. Then the progress towards the goal is defined as the reduction (in meters) from the distance to the goal region at versus at . Because is a set of nodes, we take the minimum distance as the distance between and region .
Anderson et al.  introduced a sequence-to-sequence model to serve as a learning baseline in the R2R task. We formulate a similar model to encode an entire dialog history, rather than a single navigation instruction, as an initial learning baseline for the NDH task. The dialog history is encoded using an LSTM and used to initialize the hidden state of an LSTM decoder whose observations are visual frames from the environment, and whose outputs are actions in the environment (Figure 3).
We replace words that occur fewer than 5 times with an UNK token. The resulting vocabulary sizes are 1042 language tokens in the training fold and 1181 tokens in the combined training and validation folds. We also use special NAV and ORA tokens to preface a speaker’s tokens, TAR to preface the target object token, and EOS
to indicate the end of the input sequence. During training, an embedding is learned for every token and given as input to the encoder LSTM. For visual features, we encode the visual frame using an Imagenet-pretrained ResNet-152 model and take the penultimate layer as the frame embedding.
When evaluating against the validation folds, we train only on the training fold. When evaluating against the test fold, we train on the union of the training and validation folds. We ablate the distance of dialog history encoded, and introduce a mixed planner and human supervision strategy at training time. We hypothesize both that encoding a longer dialog history and using mixed-supervision steps will increase the amount the agent progresses towards the goal.
|Seq-2-Seq Inputs||Goal Progress (m)|
|Val (Seen)||Baselines||Shortest Path Agent|
|Val (Unseen)||Baselines||Shortest Path Agent|
|Test (Unseen)||Baselines||Shortest Path Agent|
Given supervision from an end node , the agent infers navigation actions to form path
. We train all agents with student-forcing for 20000 iterations of batch size 100, and evaluate validation performance every 100 iterations (see the Appendix for details). The best performance across all epochs is reported for validation folds. At each timestep the agent executes its inferred action, and is trained using cross entropy loss against the action that is next along the shortest path to the end node . Using the whole navigation path, , as supervision rather than only the end node has been considered in other work . At test time, the agents are trained up to the epoch that achieved the best performance on the unseen validation fold and then evaluated (e.g., test fold evaluations are run only once per agent).
Recall that for each NDH instance, the path shown to the Oracle during QA exchange , , and the path taken by the Navigator after that exchange, , are given. We define the mixed supervision path as when , and
otherwise. This new form of supervision has parallels to previous works on learning from imperfect or adversarial human demonstrations. One common solution is to use imperfect human demonstrations to learn an initial policy which is then refined with Reinforcement Learning (RL). Learning performance can be improved by first assigning a confidence measure to the demonstrations and only including those demonstrations that pass a certain threshold 
. While we leave the evaluation of more sophisticated RL methods to future work, the mixed supervision described above can be thought of as using a simple binary confidence heuristic to threshold the human demonstrations.
Baselines and Ablations.
We compare the sequence-to-sequence agent to a full-state information shortest path agent, to a non-learning baseline, and to unimodal baselines. The Shortest Path agent takes the shortest path to the supervision goal at inference time, and represents the best a learning agent could do under a given form of supervision. The non-learning Random agent chooses a random heading and walks up to 5 steps forward (as in ). Random baselines can be outperformed by unimodal model ablations—agents that consider only visual input, only language input, or neither—on VLN tasks . So, we also compare our agent to unimodal baselines where agents have zeroed out visual features in place of the ResNet features at each decoder timestep (vision-less baseline) and/or empty language inputs to the encoder (language-less baseline). To examine the impact of dialog history, we consider agents with access to the target object ; the last Oracle answer ; the prefacing Navigator question ; and the full dialog history (Figure 3).
Table 3 shows agent performances given different forms of supervision. When the sequence-to-sequence agents have access to sufficient dialog history, they typically outperform unimodal ablation baselines across supervision signals. The Shortest Path agent performance with Navigator supervision approximates human performance on NDH, because is the node reached by the human Navigator after QA exchange during data collection. The sequence-to-sequence models establish an initial, multimodal baseline for NDH, with headroom remaining against human progress towards the goal, especially in unseen environments.
Progress towards the goal improves as more dialog history is added, and using the whole dialog history gives the best results on both the unseen validation and test folds for nearly every form of supervision. This supports our hypothesis that dialog history is beneficial for understanding the context of the latest navigation instruction . In both seen and unseen environments, and across dialog history ablations, sequence-to-sequence agents generally make more progress towards the goal using mixed supervision. The overall best progress in unseen validation and test environments is reached with all dialog history input, and in seen validation environments with the most recent question-and-answer pair input, both trained with mixed supervision. This supports our hypothesis that using human demonstrations only when they appear trustworthy increases agent progress towards the goal compared to using planner or human path demonstrations alone.
6 Conclusions and Future Work
We introduce Cooperative Vision-and-Dialog Navigation: 2050 human-human, situated navigation dialogs in a photorealistic, simulated environment. The dialogs contain complex phenomena that require egocentric visual grounding and referring to both dialog history and past navigation history for context. CVDN is a valuable resource for studying in-situ navigation interactions, and for training agents that both navigate human environments and ask questions when unsure, as well as those that provide verbal assistance to humans navigating in unfamiliar places.
We then define the Navigation from Dialog History task. Our evaluations show that dialog history is relevant for navigation agents to learn a mapping between dialog-based instructions and correct navigation actions. Further, we find that using a mixed form of both human and planner supervision combines the best of each: long-range exploration of an environment according to human intuition to find the goal, and short-range accuracy aligned with language input.
The sequence-to-sequence model used in our experiments serves as an initial learning baseline for the NDH task. Moving forward, formulating NDH as a sequential decision process we can use RL to shape the agent’s policy, as in recent VLN work . Dialog analysis also suggests that there is relevant information in the historical navigation actions which are not considered by the initial model. Jointly conditioning dialog and navigation history may help resolve past reference instructions like “Go back to the stairwell and go up one flight of steps,” and could involve cross-modal attention alignment.
The CVDN dataset builds on the Room-to-Room task in the MatterPort Simulator . While this simulation provides photorealistic environments, it suffers from discrete, graph-based navigation, which may be difficult to approximate for a robot agent deployed in the real world. Similar human-human dialogs collected in high-fidelity, continuous motion simulators (e.g., ) or using virtual reality technology may facilitate easier transfer to physical robot platforms. However, sharing a simulation environment with the existing R2R task means that models for dialog history tasks like NDH may benefit from pretraining on R2R.
The CVDN dataset also provides a scaffold for navigation-centered question asking and question answering tasks. In our future work, we will explore training two agents in tandem: one to navigate and ask questions when lost, and another to answer those questions. This will facilitate end-to-end evaluation on CVDN, and will differ from all existing VLN tasks by involving two, trained agents engaged in task-oriented dialog.
This research was supported in part by the ARO (W911NF-16-1-0121) and the NSF (IIS-1252835, IIS-1562364). We thank the authors of Anderson et al.  for creating an extensible base for further research in VLN using the MatterPort 3D simulator, and our coworkers Yonatan Bisk, Mohit Shridhar, Ramya Korlakai Vinayak, and Aaron Walsman for helpful discussions and comments.
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7.1 Additional CVDN Analysis
Figure 4 gives the distributions of target objects across the dialogs in CVDN. The most frequent objects are those that are both frequent across houses and typically number between 2 and 4 per house, and often have a one-to-one correspondence with bedrooms and bathrooms.
Figure 5 gives the intersection-over-union (IoU) of paths in CVDN within the same scan, comparing them against those in R2R and human performance per-dialog. The average path IoU across a scan is the average number of navigation nodes in the intersection of two paths over the union of nodes in those paths, across all paths in the scan. Compared to R2R, the paths in the dialogs of CVDN share more navigation nodes per scan because of the way starting panoramas were chosen—to maximize the distance to potential goal regions. Many CVDN paths start at or near the same remote nodes in, e.g., basements, rooftops, and lawns. Per-dialog, we measure the IoU between human Navigator and shortest path planner trajectories and find that there is substantially more overlap than between two paths in the same scan, indicating that humans follow closer to the shortest path than to an average walk through the scan (e.g., they are not just memorizing previous dialog trajectories).
7.2 Additional NDH Analysis
Figure 6 gives path data for the NDH task. Compared to R2R, path lengths using shortest path supervision () are on average shorter than those in R2R, because paths shown to the Oracle were at most length 5. By contrast, human Navigator paths () are substantially longer than those seen in R2R. We also examine the distribution of the number of hops progressed towards the goal per NDH instance across Oracle shortest path, human navigator, and mixed supervision (). While the planner always moves towards the goal (or stands still, if the Navigator is already in the goal region), human Navigators sometimes move farther away from the goal, though in general make more progress than the planner. Using mixed supervision, fewer trajectories move “backwards”; the simple heuristic of whether a Navigator walked over the last node in the Oracle’s described shortest path shifts the distribution weight farther towards positive goal progress.
7.3 Sequence-to-Sequence Model Training
We use the training hyperparameters (optimizer, learning rate, hidden state sizes, etc.) presented inAnderson et al.  when training our sequence-to-sequence agents. We adjust the maximum input sequence length for language encoding based on the amount of dialog history available: 3 for only (e.g., TAR tag, the target itself, and EOS); 70 for ; 120 for adding ; and 720 (e.g., 120 times 6 turns of history) for . We increase the maximum episode length (e.g., the maximum number of navigation actions) depending on the supervision being used: 20 for oracle (the same as in R2R) and 60 for navigator and mixed .
Teacher- versus Student-Forcing.
We use student-forcing when training all of our sequence-to-sequence agents. Anderson et al.  found that student-forcing improved agent performance in unseen environments. Further, Thomason et al.  found that agents trained via teacher-forcing were outperformed by their unimodal ablations (i.e., they did not learn to incorporate both language and vision supervision, instead memorizing unimodal priors). Thus, we see no value in evaluating multi-modal agents trained via teacher-forcing in this setting.
It is common in sequence-to-sequence architectures to reverse the input sequence of tokens during training, because the tokens relevant for the first decoding actions are likely also the first in the input sequence. Reversing the sequence means those relevant tokens have been seen more recently by the encoder, and this strategy was employed in prior work . Following this intuition, we preserve the order of the dialog history during encoding, so that the most recent utterances are read just before decoding, but reverse the tokens at the utterance level (e.g., in Figure 3 is represented as sequence “<NAV> ? upstairs go I Should”).
7.4 Naive Dialog History Encoding
We naively concatenated an encoded navigation history (via an LSTM taking in ResNet embeddings of past navigation frames) to the encoded dialog history, then learned a feed-forward shrinking layer to initialize the decoder (Table 4). We hypothesize that there is some signal in this information, but we discover that naive concatenation does not improve performance in seen or unseen environments. We suspect that a modeling approach which learns an attention alignment between the navigation history and dialog history could make better use of the additional signal.
|Seq-2-Seq Inputs||Goal Progress (m)|
|Val (Se)||Shortest Path Agent|
|Val (Un)||Shortest Path Agent|