Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue
We describe the adaptation and refinement of a graphical user interface designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot dialogue data. The data collected will be used to develop a dialogue system for robot navigation. Building on an interface previously used in the development of dialogue systems for virtual agents and video playback, we add templates with open parameters which allow the wizard to quickly produce a wide variety of utterances. Our research demonstrates that this approach to data collection is viable as an intermediate step in developing a dialogue system for physical robots in remote locations from their users – a domain in which the human and robot need to regularly verify and update a shared understanding of the physical environment. We show that our WoZ interface and the fixed set of utterances and templates therein provide for a natural pace of dialogue with good coverage of the navigation domain.
Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue
For robots to become effective teammates with humans at collaborative tasks such as search-and-rescue operations and reconnaissance, they must be able to communicate effectively with humans in dynamic environments. Ideally, these robot collaborators could engage in two-way spoken dialogue, which is both natural for humans and efficient for exchanging information about tasking, goals, situational awareness, and status updates.
To develop a robot’s dialogue capabilities, we need data on how people might talk to that robot. We collect these data through the Wizard-of-Oz (WoZ) methodology (?), used in virtual human dialogue systems to refine and evaluate the domain and provide training data for automated natural language understanding (?; ?). A critical research question is whether or not this virtual-agent approach can be extended to and effective for the physically grounded, situated language needed for communication with a robot in a collaborative task, especially if the human and robot are not co-present. Thus far, our results support the viability of this approach for our scenario. In this paper, we describe our progress, with particular attention paid to the adaptation and refinement of the wizard user interface for the research phase of collecting dialogues for training data.
The importance of the interface is twofold. First, it eases the physical and cognitive overhead of the wizard (compared to manual typing). Second, decisions on the communications built into the interface represent a critical research step: mapping the unconstrained language of a naïve participant into a set of communication intents that can be understood and acted upon by a dialogue system. The mappings, as collected in experiments, then serve to train an automated dialogue system. The interface limits communications to a fixed set, yet it must provide adequate coverage for communicating about the tasks, environment, and overcoming miscommunication. Our results show that: (i) the interface facilitates a faster pace of communication that approaches more natural dialogue exchanges and (ii) the set of utterances and templates it contains provides good coverage of the domain.
The WoZ method involves one or more human “wizards” standing in as AI modules, performing functions that will eventually be performed by a final automated system. This behind-the-scenes human activity is unknown to the research participants (so long as the WoZ illusion is successful). The WoZ methodology is useful due to its low development cost when technology to support the desired functionality does not yet exist. In the human-robot interaction domain, natural language interpretation is one of the most common use cases for WoZ (?). It has traditionally been used as a surrogate for automatic speech recognition (?; ?), and to train a dialogue system by progressively adding automation over several development stages (?).
Our setup relies on two wizards, one for dialogue management and one for robot navigation, as a result of prior pilot trials when a single wizard struggled to perform both functions (?). This setup is similar to the SimSensei project (?), where during the data collection phase, two wizards stood in for what would ultimately be separate software components (i.e., verbal and non-verbal behaviors of the virtual agent). Green et al. (?) investigated the use of multiple wizards for dialogue processing and navigation capabilities for a robot in a home touring scenario. This work found the multi-wizard approach to be valid in situations where the robot and human were co-present. We expand on this method by addressing remote (not co-present) human-robot communication.
Other research focused on developing an adaptable WoZ interface like our interface includes the SUEDE tool (?) and DOMER interface (?), both of which use a similar development strategy to ours, with iterative expansion and refinement of the responses to be included in the interface.
Background & Approach
The long-term vision of our work is to provide more natural ways for humans to interact with robots in shared tasks. The WoZ methodology facilitates a data-driven understanding of how people talk to robots in our collaborative domain. Similar to ? (?), we use the WoZ methodology only in the early stages of a multi-stage development process in order to refine and evaluate the domain and provide training data for automated dialogue system components. In all stages of this process, participants speak freely, even as increasing levels of automation are introduced in each subsequent stage or “experiment,” using data from previous experiments.
The first two experiments on the path to increased automation use two wizards: a Dialogue Manager Wizard (DM-Wizard) who sends text messages and a Robot Navigator Wizard (RN-Wizard) who teleoperates the robot. A naïve participant is tasked with instructing a robot to navigate through a remote, unfamiliar house-like environment. The participant is seated at a workstation equipped with a microphone and a desktop computer displaying information collected by the robot: a map of the robot’s position and its heading in the form of a 2D occupancy grid, the last still-image captured by the robot’s front-facing camera, and a chat window showing text responses from “the robot.” This layout is shown in the top, right-hand corner of Figure 1, which represents an overview of our WoZ setup.
At the beginning of the study, the participant receives a list of robot capabilities: the robot understands basic object properties (e.g., most object labels, color, size), relative proximity, some spatial terms, and location history. Experimenters do not give example instructions, but rather tell the participant that s/he can communicate in spoken language using natural expressions to complete tasks. In reality, the participant is speaking not to a robot, but to an unseen DM-Wizard who listens to the participant’s spoken instructions and responds with text messages in a chat window. There are two high-level response options:
1) If the instructions are clear and executable in the current physical environment, then the DM-Wizard passes a simplified text version of the instructions to the RN-Wizard, who then joysticks the robot to complete the instructions.
2) If the instructions are problematic in some way, due to ambiguity or impossibility given either the current physical context or the robot’s capabilities, then the DM-Wizard responds directly to the participant in text via a chat window, in order to clarify the instructions and/or correct the participant’s understanding of the robot’s capabilities.
We engage each participant in three sessions: a training task and two main tasks. The two main tasks, lasting 20 minutes each, focus on slightly different search and analysis subtasks and start in distinct locations within a house-like environment. The subtasks were developed to encourage the participant to treat the robot as a teammate who helps search for certain objects, but also to encourage the participant to tap into their own real-world knowledge to analyze the environment. An example search task was to locate shoes, relying on robot-provided images. An example analysis task was to consider whether the explored space was suitable as a headquarters-like environment.
The human-robot communication data collected in Experiments (Exp) 1 and 2 allow us to incrementally build a fully automated dialogue system on-board a physical robot. In Exp 1, our goal was to elicit the full range of communications that may arise in our domain. To allow for this, the DM-Wizard simply typed free responses to the participant following guidelines established during piloting that governed the DM-Wizard’s real-time decision-making (?). The guidelines identified the minimal requirements for an executable instruction: each must contain both a clear action and respective endpoint. The guidelines also provided response categories and templates, giving the DM-Wizard easily-remembered templates for elements of each response, but also flexibility in exact word choice. Data from ten participants was collected in Exp 1.
The Exp 1 data was then analyzed to develop a set of communications to design into an interface for Exp 2, where messages sent via that interface would strike a balance between tractability for an automatic system and full coverage of the domain, including recovering from problematic instructions. With the interface, instead of typing free responses, the DM-Wizard constructs a response by selecting buttons on a graphical user interface (GUI), where each button press sends a text message either to the participant or the RN-Wizard. Aside from the DM-Wizard communicating via the interface, Exp 2 was conducted just like Exp 1. Ten new participants took part in Exp 2.
Before delving into the development of this interface that preceded running Exp 2, we should note that we have already completed Exp 2 and used the collected data to train a classifier that automatically generates responses to particular participant instructions. As a result, it is quite clear that additional training data is needed for a robust classifier and so in the planned Exp 3, we are simulating both the physical environment and the robot (?). The simulation will allow us to reduce the time and space overhead otherwise needed for performing this experiment using a physical robot in a real environment; therefore speeding up the collection of training data. As we collect adequate levels of training data, in future experiments we will begin to automate away individual components, currently using wizard stand-ins. For further discussion of this development process, see Ongoing & Future Work.
The DM-Wizard interface, which is the key change in the setup between Exps 1 and 2, sends a text response to either the participant or the RN-Wizard. The critical research challenge in developing this interface has been to capture the sum total of possible responses “the robot” can give to the RN-Wizard and participant, whose language is totally unconstrained and can vary widely. Thus, the quality of the trained model is contingent upon the GUI design decisions. However, the goal of domain coverage had to be balanced with the need to create an interface that was organized such that the wizard could easily and quickly find the appropriate button – presenting another development challenge.
The interface software was adapted from a design used for WoZ prototyping of a dialogue system in which humans can engage in time-offset interaction with a WWII Holocaust survivor (?). In that application, people could ask a question of the system, and a pre-recorded video of the Holocaust survivor would be presented, answering the question. The interface is implemented as a web application that presents a collection of clickable buttons in a web browser window. In our study, the DM-Wizard uses the interface buttons to trigger a text response to be sent to the appropriate chat window in either the participant’s screen or the RN-Wizard’s screen. The system uses the VHMsg messaging protocol (?) built on top of the ActiveMQ message broker.111http://activemq.apache.org The rest of the system components, including tools for logging, data visualization, and robot operation, interact via ROS (Robot Operating System).222http://www.ros.org We also implemented a software bridge that connects to VHMsg and the ROS message server and maps VHMsg messages onto ROS messages and vice versa automatically.
There are a large number of responses, and therefore buttons, needed in the interface to provide coverage for all of the participant instructions that must be passed to the RN-Wizard, and for all of the possible responses needed to clarify or acknowledge instructions. To organize the large number of buttons (that do not fit in a single screen), there are tabs to switch between five screens, which present thematically related buttons. Within each screen, there are labeled rows of buttons, which represent subthemes. As needed, a very frequently used button (e.g., “done”) may appear in more than one location to speed up the DM-Wizard’s ability to find it. Color-coding and a short label on the button aid in quick identification, and the full content of the message associated with a button can be viewed by hovering over it. A snippet of the interface is shown in Figure 2.
The initial WoZ design assumed that the message templates are static and can be fully configured before running the system. In our preliminary analysis, we observed that a significant proportion of DM-Wizard messages fell into well-defined patterns or templates, e.g., “I see a door on the left,” “I see a door on the right,” “I see a wall,” etc. In such cases, we observed that it would be difficult to enumerate all but the most frequently occurring objects in the scenario. Thus, we extended the WoZ interface to allow the DM-Wizard to modify the message content on the fly. Specifically, a button message text may now include a text-input slot. When the DM-Wizard clicks on such a button, a pop-up window appears with a text-input field, e.g., “I see ___.” The DM-Wizard types the object description and sends the newly completed message to its recipient. Note that there is no entirely open response button, all buttons reflect, at a minimum, an observed template of responses like “I see ___”. Other text-input slots are shown in all caps in Figure 2.
We used an iterative approach to develop the final interface. This was carried out by testing it in practice runs in which we replayed audio from Exp 1, followed by a round of real-time Exp 2 pre-piloting. The refinements can be broadly categorized into changes that affected the content of the interface (i.e. the messages associated with buttons) and changes in the layout of the buttons.
The finite set of options included in the DM-Wizard interface were carefully selected to strike a balance between providing enough expressive options for the DM-Wizard to pass along executable instructions and overcome communication problems, and limiting the options to a set that is tractable for automation. Although the WoZ methodology using a fixed set of messages has been shown to be effective for communication with virtual agents, the adaptation of this approach to human-robot dialogue explored a new research question: whether this approach is viable for situated language, where a shared understanding of the physical environment, including objects, locations, paths and orientations, is needed, particularly when the human and robot are not co-present.
Initial Content – To gain a preliminary understanding of the messages needed in the interface, we undertook thorough analysis and annotation of the Exp 1 data, in which the DM-Wizard had typed relatively free responses to the participant. The DM-Wizard responses were categorized first according to their recipient (either the participant or the RN-Wizard), and then by their function (e.g., feedback, clarify-distance, describe-capability) (?). These annotations were used to create the first version of the interface, in which an effort was made to ensure that each DM-Wizard message occurring 2 or more times in Exp 1 was captured in the interface. For communications that occurred very frequently, alternatives were added with slightly different lexical choices. These can be alternated as needed to reduce the potentially unnatural, repetitive feel of the robot’s communications.
Metric References – While adding the appropriate button was straightforward for many of the common messages sent to the participant (e.g., “I’m not sure which object you are referring to,” “Executing,” “Done”), this process was not as straightforward for instruction messages sent to the RN-Wizard, which regularly drew upon the physically grounded, situated nature of the language in this domain. Common instructions include asking the robot to move forward a particular number of feet, rotate left or right a particular number of degrees, and moving to an object or landmark in the physical surroundings (?). Despite these commonalities, providing coverage for these instructions in the interface was challenging given that the specific number of feet (or another unit of measurement) and degrees would vary. In lieu of including a button for every possible unit of movement, we took advantage of the interface capability for buttons with text-input fields, which allow the DM-Wizard to type in the specific number of feet or degrees requested. Because use of this type of button is more time-consuming, very common measurements found in the instructions were given their own, dedicated button (e.g., “turn left 90 degrees,” “move forward two feet”). Nonetheless, having some flexibility in certain common instructions allows for the generalization needed to avoid an over-proliferation of buttons that would be unmanageable for the DM-Wizard.
Location & Object References – Although it was also possible to use buttons with text-input fields to cite specific objects and landmarks used in instructions, we decided to create a map of our physical environment and assign unique identifiers to all spaces (i.e. rooms, hallways), objects, and doorways. We then added buttons accommodating instructions to move to/through/parallel to each doorway in the environment, move to each object in the environment, and move into each specific room and hallway space in the environment. Thus, for example, if a participant instructed the robot to “go ahead to the office/black/padded chair ahead,” this would be translated to an RN-Wizard instruction using a specific identifier, e.g., “move to Conference Room Right Chair 1.” Although this more than doubled the number of buttons in the interface, our hope was that this would make our training data more tractable for a preliminary system in which the environment is known, and particular locations and objects are assigned coordinates in that environment. To make the larger number of buttons more manageable, instructions using unique identifiers associated with different spaces in the environment were separated out into different tabs/screens (see top row of tabs in Figure 2).
Feedback Messages – In Exp 1 participants remarked on the slow pace of the experiment. Even in cases of entirely successful communications and executable instructions, the relatively slow pacing of the dialogue could lead the participant to believe that something had gone wrong. The introduction of an interface in Exp 2 partially addresses this problem simply because it allows the DM-Wizard to pass messages more quickly without typing. We also wanted to provide more transparency to participants as to what the robot had understood and what it was doing, ensuring they could be confident in the success of their communications. Thus, a major area of content additions to the interface were different types of feedback and back-channeling to improve the transparency of what the robot was doing (?) and help to establish common ground (?).
In Exp 1, the robot would often only respond with “Executing” when a set of instructions was underway and “Done” when instructions were completed. In Exp 2, the GUI allowed us to make this feedback much more nuanced. For clear cases of executable instructions, the DM-Wizard would immediately acknowledge that the instructions were heard and understood by responding with “ok” followed by a demonstration of that understanding in a repetition or paraphrasing of the instructions given. This repetition was either in the form of a description of what the robot was about to do, or a description of what the robot just did: “I’ll move forward three feet,” or “I moved forward three feet.” On the other extreme, in cases where the executability of the instructions was very uncertain and the DM-Wizard needed time to consider the surrounding physical environment, the immediate response was “Hmmm…,” which could be followed either by a confirmation of the intention to complete the instructions or a description of the problem and a suggestion for an alternate, potentially helpful action (e.g., “There’s an obstruction preventing me from doing that. Would you like me to send a picture?”). We can anecdotally report that these feedback additions seemed to help the robot to hold the floor when instructions were being completed – participants seemed less likely to assume something had gone wrong and abandon one strategy of instructions in favor of a new strategy (quantitative verification of this observation is underway).
Decomposing Complex Instructions – Another area of major content changes was the decomposition of complex instructions to the RN-Wizard. At this stage of our research, we have not fully fleshed out how the instructions passed to the RN-Wizard could be translated into policies executable by a robot’s planning component. Nonetheless, in the development of the interface, we were forced to consider what policies might be associated with each button that passed a particular set of instructions to the RN-Wizard. For example, the interface button “Move to Kitchen Door” was originally associated with a policy of (i) moving to the vicinity (within one robot’s length) of the referenced door and then (ii) orienting to face the door – thus this was a “complex” instruction in the sense that it was associated with more than one move/turn action. Although we had hypothesized this sequence was what most participants would want the robot to do given this instruction, in pre-piloting we found that participants often used doorways as landmarks for moving certain distances, but they weren’t always interested in the doorway itself. In these cases, they might say something like “Move parallel to the doorway ahead on the left,” an action that we couldn’t execute precisely since our set of instructions always included a policy of both moving and orienting to face a referenced doorway.
We discovered that the messages to the RN-Wizard could be much more flexible and composable if we limited the number of complex instructions (containing more than one move/turn instruction) included, and decomposed most into individual buttons/instructions associated with only one action. In the case of doorways, we opted to associate “Move to Kitchen Door” with moving into the vicinity (within one robot’s length) of the referenced door only. We then added buttons for each door that accommodated instructions to move parallel to each door and turning to face each door in the environment. In general, the flexibility afforded by decomposing messages into the smallest action or intentional units led to similar decomposition in the DM-Wizard to participant communications.
During development, we changed the layout of the buttons and screens by shifting from a thematically related layout to one that better accommodated which buttons were often used together in a series in practice. The best example of this allowed for the DM-Wizard to quickly communicate instructions to the RN-Wizard and corresponding feedback to the participant. In the original formulation of the interface, one screen was dedicated for communications to the participant, and a second screen for communications to the RN-Wizard. This layout forced the DM-Wizard to constantly switch screens in order to first provide instructions to the RN-Wizard and then feedback to the participant. In answer to this challenge, the screen previously dedicated to communications to the RN-Wizard was augmented with color-coded buttons that sent feedback to the participant. So, for example, next to the “Move forward three feet” button with instructions to the RN-Wizard, we added feedback buttons “I will move forward three feet” or “I moved forward three feet” to the participant. Examples can be seen in Figure 2, where color coding indicates the message recipient. This altered layout made it easier for the DM-Wizard to provide timely and specific feedback to the participant. In the future, we are considering the introduction of buttons that simultaneously send one message to the RN-Wizard, and another corresponding feedback message to the participant.
Impact of Interface
The introduction of the interface in Exp 2 impacted the dialogue data collected by limiting DM-Wizard utterances to the coverage available in the interface buttons, and by speeding up the pace of dialogue.
In development, an effort was made to ensure that the Wizard interface provided coverage for all DM-Wizard messages with two or more occurrences collected in Exp 1. Out of a total of 2,728 DM-Wizard messages in Exp 1, there were 2,075 that occurred two or more times with 84 unique messages covering 76% of the total. This regularity is expected, and arises in part from the nature of the domain and task, but also from the DM-Wizard being guided by the policies and templates for responses given in the Exp 1 guidelines. Of course, the singletons that we did not accommodate with a dedicated response button still require some type of response, so these were handled through one of the following strategies:
1) buttons with text-input fields accommodate less frequent measurements in common move or turn-type instructions;
2) buttons with somewhat generalized vocabulary are available for clarifications (e.g., “the one on my left?” provides coverage for a more specific response in Exp 1: “the crate on my left?”); and
3) buttons with very generic indications of the problematic nature of an instruction (e.g., “I’m not sure what you are asking me to do; can you describe it another way?”) accommodate truly novel cases that may be off-topic, entirely outside of the robot’s represented capabilities, or very ambiguous and/or nonsensical given the physical environment, (e.g., “rotate left 200 feet”).
The final interface contained 404 buttons, organized into 5 screens, where the first two screens convey the most common recipient for buttons on that page (either the participant, called “Commander,” or the RN-Wizard), and the last three screens contained buttons that reference different objects/areas in the physical environment.
After the iterations of interface refinement were completed, its coverage was analyzed using string matching to compare all of the DM-Wizard messages in Exp 1 to the messages included in the finalized interface. We found that 88.7% of the messages in Exp 1 have an exact match in the interface buttons, while 10.5% have partial matches with fairly clear candidates for the best interface button, and 0.8% have no match and no clearly corresponding interface button. In qualitative analysis, we see that the partial matches generally reflect cases handled with buttons with text-input fields or more general vocabulary (strategies (1) and (2) above), while the no-match cases are places where a more generic strategy of non-understanding would be used (strategy (3)). See Appendix for examples of parallel Exp 1 and 2 DM-Wizard responses.
Because of the DM-Wizard’s cognitive and physical overhead, the pacing of dialogue in Exp 1 was quite slow.333Observed DM-Wizard delays lasted well beyond 700 ms pause thresholds in spoken dialogue systems (?). Although both the experimenter and “the robot” explain to the participant that “there may be lag times,” the slow pacing certainly affected the participant’s perception of the robot as an interlocutor. Schegloff (2007) notes that “preferred” responses in dialogue tend to occur immediately after a single beat of silence that is the normal transition place from one interlocutor to another. On the other hand, a common feature of “dispreferred” responses in dialogue is that they are delayed – there is a longer period of silence prior to the response. Thus, human interlocutors are tuned to understand timely feedback as an indication that everything is going well in the interaction, while silence can indicate a problem. Given these features of natural dialogue, we felt it important to speed up the pace of the interaction and avoid long silences where the participant was not receiving feedback as to what the robot was doing. The introduction of the interface facilitates faster responses and, since typing isn’t needed, the feedback can also be longer and more specific (e.g., “Ok, I’ll move forward three feet,” as opposed to “Executing,” in Exp 1).
Currently, we are processing the data needed to examine exact differences in response times between Exps 1 and 2. However, we can gain a sense of the faster pace of dialogue in Exp 2 by examining how much the 10 participants were able to accomplish in Exp 1 compared to Exp 2. The time allotted (two 20-minute phases) and tasks in both experiments are the same; tasks can be broadly categorized as search and navigation tasks. Thus, most instructions are either requests for the robot to move or turn (navigation), or to send a picture of the current environment to see if it includes target objects of interest (search). Anytime a move or turn action was successfully completed by the robot, completion feedback was given (e.g., “done”). Similarly, anytime a request for a picture is fulfilled, completion feedback was given in the form of “sent.” We can therefore report the number of times this type of completion feedback is given as a proxy measure for the pacing of dialogue. In Exp 1, across all 10 participants, there are 829 instances of completion feedback, indicating that 829 task-oriented actions were completed. In Exp 2, there are 1069 instances of completion feedback – indicating that participants were able to successfully complete more subtasks in Exp 2. Admittedly, this is a flawed measure, given that the participants may individually differ in how they approached the task and how successful they were. Nonetheless, it does reflect a trend in Exp 2 towards faster-pacing, which is closer to a natural human dialogue pace and which allows participants to complete more tasks in the allotted time.
Ongoing & Future Work
Creating the interface was our first step in automating the robot partner by providing a more advanced tool for the DM-Wizard’s natural language generation tasks. We plan to automate additional tasks, ultimately removing the wizards.
Automating the Dialogue Manager – We are currently investigating use of the NPCEditor (?), which has been used extensively for virtual human applications (typically question-answer systems). It performs natural language understanding by learning a mapping between input utterances and their associated responses. In our case, the utterance-response mappings will be extracted from our experimental data.
We expect the more structured language output from the GUI to simplify this process.
Automating the Robot Navigator – In the current experiments, we rely on human intelligence to interpret high-level instructions including metric information and landmarks. While these instructions clearly specify some aspects of the endpoints, they are often underspecified in terms of the exact path to be taken and the final specific location and orientation. We are designing a set of policies for a robot planner in order to interpret these high-level instructions and guide the robot to the appropriate endpoints. Since the path and final positioning derived by an automated planner might be different from that taken by a human RN-Wizard, we may need additional feedback to and clarification from the participant. We are testing variations of the observed instructions and paths taken in order to provide a general action capability.
The progress described here demonstrates
that WoZ approaches taken in virtual human applications
can also be successfully adapted to this new domain
of human-robot dialogue,
where the interlocutors are not co-present and must rely on
a shared understanding of the physical surroundings.
Although we are in the early stages of our research,
the implementation of a Wizard interface discussed here represents
milestone in establishing the feasibility of this approach.
a great deal of flexibility in how “the
robot” interacts with the physical environment and
handles various naïve participants’ strategies in completing
search and navigation tasks.
We have shown that the interface provides coverage
of the domain with a limited set of messages and templates,
carefully crafted to generalize over distinct contexts.
Additionally, the interface has been crucial in eliciting more natural
dialogue since it allowed for faster-pacing of turns, to which
humans are accustomed.
This research was sponsored by the U.S. Army Research Laboratory.
Appendix: Dialogue Samples
The following dialogue snippets illustrate the difference between Exp. 1 “free” response mode and Exp. 2 GUI-structured Response Mode. The dialogues are between a Participant (P) and DM-Wizard (DM). Note the increased consistency of DM-Wizard responses to the open-ended instructions “move forward” in Exp. 2.
Exp. 1 Free Response Mode
You can tell me to move to something that you see.
go forward four feet
continue to move down the hallway
How far should I continue down the hallway? Would you like me to reach something that you see or go a certain distance?
go down the end of the hallway where it appears to be a clock on the wall
Exp. 2 GUI-Structured Response Mode
robot go forward
I’m not sure where or when to stop moving forward. You can tell me to move a certain distance or to move to an object.
robot move forward one feet
can you move forward
I’m not sure where or when to stop moving forward. You can tell me to move a certain distance or to move to an object.
can you stop at the second door
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