Abstract

Human-robot interfaces (HRI) can be difficult to use. We examine urban search rescue robots (USR) as an example. We present here a theory of their use based on a simulated user written in the ACT-R cognitive modeling language. The model, using a simulated eye and hand, interacts directly with an unmodified and simple tele-operating task of maneuvering in an environment to avoid other moving objects. The model user also performs a secondary task. In addition to describing the knowledge the human operator must have, as well as what aspects of the task will be difficult for the operator, the model makes quantitative predictions about how the speed of the robot influences the quality of the navigation and performance on the secondary task. These results are examples of the types of outputs available from a model user. As the model now interacts with the USR simulator using only the bitmap, the model should be widely applicable to testing other simulators and to actual robots. The model already suggests why human-robot interfaces are difficult to use and where they can be improved.

Index Terms-- cognitive model, ACT-R, human-robot interfaces

I. INTRODUCTION

In the future, it might be that robots will become completely autonomous and will act largely independent. However, such a level of independence has not yet been achieved and is in some cases simply undesirable. Many of the tasks that robots face today like exploration, reconnaissance, and surveillance, will continue to require supervision [1]. Furthermore, people often do not have enough confidence in a completely autonomous robot to let it operate independently. So it seems that the level to which the use of robots will be integrated in our society, will be largely dependent on the robots ability to communicate with humans in understandable and friendly modalities [2].

Despite its importance, a general theory of human-robot interface use seems to be lacking. Many human-robot interfaces do not even respect the most fundamental HCI principles. In this paper, we will present the beginnings of a theory that indicates the issues that make human-robot interfaces difficult to use. Concurrently, we will present a quantative tool in the form of a simulated user that can be used to identify problems associated with human-robot interface use. Specifically, we introduce a methodology in which a cognitive model autonomously exercises human-robot interfaces, indicating ways to improve the interface and

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Using a simulated user to explore human robot interfaces

D. VAN ROOY, FRANK E. RITTER Member, IEEE, and R. ST.-AMANT, Member, IEEESimuser to explore HRI

Manuscript for Special Issue on Human-Robot Interaction - IEEE Transactions on Systems, Man and Cybernetics.

laying bare problems that can serve as starting points for a general theory of human-robot interface use.

One of the reasons that there does not seem to be a general theory of human-robot interface use is the complexity of the task domain, which is reflected in the diversity in types of human-robot interactions. An application that illustrates this well is that of robot assisted Urban Search and Rescue (USR). USR involves the detection and rescue of victims from urban structures like collapsed buildings. Because of the extreme physical and perceptual demands of USR, these applications are usually mixed-initiative human-robot interactions, in which a human operator and a robot interact in some manner to produce adequate performance [3]. This means that it might be optimal for the robot to exhibit a fair amount of autonomy in some situations, for instance, in navigating in a confined space using its own sensors. However, other situations might require human intervention: An operator may have to assist in freeing a robot because its sensors do not provide enough information for autonomous recovery [3]. And yet further interventions, some only imagined, such as providing medication to trapped survivors, will legally require human intervention. This illustrates how in the case of enhanced robot autonomy, the role of the operator could often shift between control to monitoring and diagnosis [1].

There are several reasons why principles from HCI are missing from many human robot systems. First of all, the task domain of human-robot systems is more complex and diverse, making it very hard to meet the needs of diverse users or come up with a general metaphor. Furthermore, these systems are typically more expensive than regular commercial software packages. At the same time, they are not built as often as regular software and when they are built, it is usually not by people trained in HCI. Currently, USR robots are directly driven by operators. As they become more autonomous, these problems will become more complex. What is needed is a way to test and improve these interfaces.

II. USING A SIMULATED USER TO EXPLORE HUMAN ROBOT INTERFACES

In this section, we will introduce a cross-platform architecture in which a cognitive model simulates user performance. Specifically, we will introduce a simulated user, consisting of a cognitive model and a pair of simulated eyes and hands that can be applied to sample human-robot interfaces (or with additional knowledge any other interface for that matter). Ultimately, the intention is to provide a quantative tool to guide the design process of human-robot interfaces. This tool will enable designers to apply psychological theories in real time, providing a simulated user that acts like and interacts with the same interface as a real user.

A cognitive model forms the cognition of our simulated user. A cognitive model is a theory of human cognition realized as a running computer program. It produces human-like performance in that it takes time, commits errors, deploys strategies, and learns. It presents a means of applying cognitive psychology data and theory to HCI problems in real-time and in an interactive environment [4-6]. We have developed a system consisting of the cognitive architecture ACT-R [7] and a simulated eyes and hands suite called Segman [8] that can be applied to virtually

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any type of interface running on any operating system. We will begin by describing the parts that make up the system and then provide a demonstration. Subsequently, we’ll discuss how this system can be applied as a simulated user to explore human-robot interaction, and how it supports explanations of user’s behavior and evaluation of interfaces.

A. The ACT-R architecture

The ACT-R architecture integrates theories of cognition [7], visual attention [9], and motor movement [10]. It has been applied successfully to higher-level cognition phenomena, such as modeling scientific reasoning [11], differences in working memory [12], and skill acquisition [13] to name but a few. Recently it has been applied successfully to a number of HCI issues [14] [15] [6].ACT-R makes a distinction between two types of long-term knowledge, declarative and procedural. Declarative knowledge is factual and holds information like “2 + 3 = 5” or “George Bush is the president of the USA”. The basic units of declarative knowledge are chunks, which are schema-like structures, effectively forming a propositional network. Procedural knowledge consists of production rules that encode skills and take the form of condition-action pairs. Production rules correspond to specific goals or sub-goals, and mainly retrieve and change declarative knowledge.

Besides the symbolic procedural and declarative components, ACT-R also has a sub-symbolic component that determines the use of the symbolic knowledge. Each symbolic construct, be it a production or chunk, has sub-symbolic parameters associated with it that reflect its past use. In this way, the system keeps track of the usefulness of the symbolic information. Which information is currently available in the declarative memory module is partially determined by the odds that a particular piece of information will be used in that context.

An important aspect of the ACT-R architecture is that models created in it predict human behavior qualitatively and quantative: Each covert step of cognition (production firing, retrieval from declarative memory, procedural knowledge application) and overt action (mouse-click, moving visual attention) has latencies associated with them that are based on psychological theories and data. For

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Figure 1. ACT-R 5 system diagram. The production system and buffers run in parallel, but each component is itself serial. The graded areas indicate the novel functionality provided by SEGMAN that overrides the original perceptual –motor functionality of ACT-R 5, which is indicated by the dashed lines.

instance, taking a cognitive action, firing a production rule, takes 50 ms (modulated by other factors such as practice), and the time needed to move a mouse is calculated using Fitts law (e.g., [16]). In this way, the system provides a way to apply psychological knowledge in real-time.

B. The perceptual-motor buffers

A schematic of the current implementation of the theory, ACT-R 5.0 (act.psy.cmu.edu/ACT-R_5.0), is shown in Figure 1. At the heart of the architecture is a production system, which represents central cognition and interacts with a number of buffers. These buffers represent the information that the system is currently acting on: The Goal buffer contains the present goal of the system, the Declarative buffer contains the declarative knowledge that is currently available, and the perceptual and motor buffers indicate the state of the perceptual and motor module (busy or free, and their contents). The communication between central cognition and the buffers is regulated by production rules. As mentioned, production rules are condition-action pairs: The first part of a production rule, the condition-side, typically tests if certain declarative knowledge (in the form of a chunk) is present in a certain buffer. The second part, the action side, then sends a request to a buffer to either change the current goal, retrieve knowledge from a buffer such as declarative memory, or perform some action.

The perceptual and motor buffers allow the model to “look” at an interface and manipulate objects in that interface. The perceptual buffer builds a representation of the display in which each object is represented by a feature. Productions can send commands to the perceptual buffer to direct attention to an object on the screen and create a chunk in declarative memory that represents that object and its location on the screen. The production system can then send commands, initiated by a production rule, to the motor buffer to manipulate these objects.

Central cognition and the various buffers run in parallel with one another, but each of the perceptual and motor buffers is serial (with a few rare exceptions) and can only contain one chunk of information. This means that the production system might retrieve a chunk from declarative memory, while the perceptual buffer shifts attention and the motor buffer moves the mouse. We will mainly concentrate on the motor and perceptual buffer, which are most relevant for our purpose.

C. Segman and ACT-R 5

ACT-R 5 in its current release (act.psy.cmu.edu) interacts with interfaces using a Perceptual-Motor buffer (ACT-R/PM). ACT-R/PM [15] includes tools for creating interfaces and annotating existing interfaces in Macintosh Common Lisp so that models can see and interact with objects in the interface. This allows most models to interact in some way with most interfaces that are written in that language, and to let all models interact with all interfaces written with the special tools.

For our simulations, we developed a more general version of ACT-R/PM, which provides ACT-R 5 direct access to an interface, thus removing the need for a specific interface creation tool. This is done by extending ACT-R/PM with the Segman suite

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(www.csc.ncsu.edu/faculty/stamant/cognitive-modeling.html).

As Figure 1 shows, Segman [8] takes pixel-level input from the screen (i.e., the screen bitmap), runs the bitmap through image processing algorithms, and builds a structured representation of the screen. This representation is then passed to ACT-R through the ACT-R/PM theory of visual perception (i.e. perceptual buffer). ACT-R/PM moderates what is visible and how long it takes to see and recognize objects. Segman can also generate mouse and keyboard inputs to manipulate objects on the screen. This functionality is called through the ACT-R/PM theory of motor output, but we have extended the output results to work with any Windows interface. This is done by creating very primitive events (click icon, select button, etc), which are implemented as functions at the operating system level. As such, they are indistinguishable from human-generated events. Currently, we have a fully functional system that runs under Windows 98 and 2000.

III. THE MODEL OF ROBOT DRIVING

We will now describe an implementation of our system called DUMAS (pronounced [doo ‘maa], see also smartAHS [17]), which stands for Driver User Model in ACT-R & Segman. DUMAS drives a car in a Java-implemented game, which was downloaded from www.theebest.com/games/3ddriver/3ddriver.shtml. For the simulations reported below, no changes were made to the game.

We choose the 3D driver game for several reasons. First, it has a direct interface, in that the operator directs the car using the keyboard. This perspective is often referred to as “inside-out” driving, because the operator feels as if she is inside the vehicle and looking out, and is a common method for vehicle or robot tele-operation [1]. Second, driving behavior is a prototypical example of real-time, interactive decision making in an interactive environment [18] [14] and is as such is comparable to many tele-operated robot tasks. The source code is extensible, which means aspects of the environment (e.g., slow or fast driving) and interface can be manipulated (e.g., bigger or smaller buttons), in a controlled fashion. Because the code is Java, this can be done on multiple platforms. And finally, because we did not write it, it helps to show the generality of this approach.

Models of driving have been targets of research for decades (the analysis of Gibson and Crooks in 1938 provides one of the earliest examples [19]; see Bellet and Tattegrain-Veste [20] for a concise historical overview from a cognitive ergonomics perspective.) The hierarchical risk model of van der Molen and Botticher is a representative example of recent models [21]. Driving can be seen as structured into strategic, tactical and operational levels. Moving up the hierarchy, each level describes an increasingly abstract set of behaviors that govern choices at the level below it. At the strategic level, planning activity takes place, such as the choice of route and travel speed. At the tactical level decisions encompass more concrete, situation-dependent actions, such as lane changing, passing, and so forth. The operational level describes skilled but routine activities, such as steering and acceleration.

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The different levels of abstraction represent different demands on the cognitive, perceptual, and motor abilities of the driver. For example, feedback from assistive technology such as ABS or power steering is provided at the operational level through haptic channels, often imperceptibly. Feedback for travel speed, in contrast, requires some cognitive activity at the strategic level, to interpret speedometer readings. If the feedback channels from these different activities were reversed (e.g., if the driver had to interpret a numerical value to determine power steering assist), their usability would be seriously impaired. Many task domains in HRI, in particular urban search and rescue, share this layered structure.

A. Current implementation of the model

We set out to let the model perform some standard tasks, like staying on track, avoiding traffic and increasing or decreasing speed. At this point, the model can start the game by clicking the mouse on the game window, accelerate by pushing the “A” key, brake by pushing the “Z” key, and steer by using the left and right arrow-keys.

Perceptual processing in the model is based on observations from the literature on human driving, as is common for other driving models. Land and Horwood's [22] study of driving behavior describes a "double model" of steering, in which a region of the visual field relatively far away from the driver (about 4 degrees below the horizon) provides information about road curvature, while a closer region (7 degrees below the horizon) provides position-in-lane information. Attention to the visual field at an intermediate distance, 5.5 degrees below the horizon, provides a balance of this information, resulting in the best performance.

The visual interface of the 3D Driver Game, which is the same interface the model uses, is shown in Figure 2. The default procedure for perception in the model is as follows. The model computes position-in-lane information by detecting the edges of the road and the stripes down the center of the road. The stripes are combined into a smoothed curve to provide the left boundary of the lane, while the right edge of the road directly gives the right lane boundary. The model computes the midpoint between these two curves at 5.5 degrees below the horizon. This point, offset by a small amount to account for the left-hand driving position of the car, is treated as the goal. If the center of the visual field (the default focal point) is to the right of this point, the model must steer to the right, otherwise to the left.

Perceptual processing in the model has limitations. For example, it is not entirely robust: Determining the center of the lane can break down if the road is curving off too fast in one direction. Segman can also return some of the information that it has extracted. For example, it can determine road curvature from more distant points, as is done in models of human driving [23]. However, this has not yet led to improved performance in this simulation environment.

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In its current form, the model has problems staying on the road because visual cognition is not yet perfect. The amount of change in speed depends on how the visual environment is changing. At the moment, the model takes snapshots of the whole visual scene to determine its actions. It detects changes by recording the locations of specific points in the visual field and then measuring the distance they move from one snapshot to the next. It turns out that this is not a good way to handle visual flow: Suppose that at time t the model analyzes the road, records the data for estimating visual flow, and determines that steering one direction or another is appropriate. At time t+1 some steering command is issued, and the simulated car moves in that direction. At time t+1 or later the road is again analyzed so that flow can be computed, but at this point the action of the model resulted in changes in the visual field, independent of changes that would have occurred otherwise. This contribution needs to be accounted for, or the car might end up braking every time it steers.

The model still represents a rather restricted model of driving behavior. Whereas previous driving models use more than 40 rules [18], the complete behavior of DUMAS is currently determined by only 20 productions rules. Foremost, this reflects that the production system of DUMAS does not yet use the full range of perceptual-motor capabilities offered by the ACT-R architecture through the ACT-R/PM theory. Nevertheless, the demonstrations below will illustrate that even with a relatively simple ACT-R model, the current model already demonstrates some of its capabilities and produces behavior that is fully in line with more established models of driving.

b. Two demonstrations

We provide two example analyses of the 3D driver game interface. This first one assesses the influence of speed on the ability to drive, the second examines how multi-tasking influences driving. These demonstrations are really proofs of existence. They are examples of the type of measures that would be helpful in testing and designing more advanced human-robot interfaces. In order to increase the realism of our simulation, we will need to expand the perceptual-motor capabilities of the ACT-R model. However, even though the model at this point only simulates constraints in cognitive functioning, it is able to simulate realistic driving behavior. Figure 3 shows a screenshot of the desktop during a simulation run. On the left, is a GNU Emacs window, in which a trace of the cognitive model appears

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Figure 2: Two snapshots of the driving environment.

(which we discuss later). The right top half of the figure shows the debug window of the Allegro Lisp package.

DUMAS starts the game autonomously by clicking on the game window shown in the right bottom of the figure. Note that the model “knows” where the gaming window resides on the desktop. By limiting the attention of the model to the position and dimensions of the game, we create a virtual bounding box on the screen.

Next, the model accelerates, drives at a constant speed and slows down if necessary (e.g. in a strong curve). Because at this point the model cannot pass, a run typically ends when the model hits traffic in its own lane. Runs can also end when it commits an error and runs off the road. Figure 4 shows the results of the model simulation on two dependent measures: Lateral deviation, which shows the position of the car with respect to the center of the right lane and total driving time in minutes. These should be seen as example measures. The model and architecture can provide other measures such as working memory load, spare capacity in the processor and learning.

Speed

In the speed demonstration, DUMAS completed three sets of 10 runs, one at low, one at medium and one at high speed, in the 3D Driving Game. We looked at the influence of speed on total driving time and the amount of lane deviation, which reflects the ability of the model to stay on its ideal line of driving. Lane deviation is commonly used in driving studies to measure the influence of factors such as multi-tasking and drug use [24]. Figure 4 summarizes the results.

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Figure 3: Screen capture showing a GNU Emacs window on the left, an Allegro Lisp window in the top right corner and the driving game in the bottom

The left panel of Figure 4 shows the model predicts that average lane deviation will increase as speed increases, which is in line with experimental data and previous models . The model needs a certain amount of time to update its representation of the environment, mainly determined by constraints build into the ACT-R model. As a result, the distance between steering adjustments increases as speed increases thus leading to larger lane deviations.

The right panel of Figure 4 shows how total average driving time, measured in minutes, drops significantly as speed increases. The explanation for this is made clear by the type of errors in the first condition: Because more distance passes between two steering adjustments, the chance of accidents also increases. In the Slow condition, DUMAS only had 3 accidents, compared to 7 and 10 in the Medium and Fast conditions respectively.

Multi-tasking

In the multi-tasking demonstration, we illustrate how dividing the model’s attention produces the same effect as increasing speed. In essence, the model’s performance is determined by the speed and accuracy with which it reacts and adapts to the environment. As a consequence, anything that diverts attention from driving will affect performance. More precisely, the time between updating moments will increase, leading to behavior that is less adapted to the environment.

To simulate the influence of anxiety as a dual-task, we added useless knowledge to the system designed to interfere with driving. Specifically, we added to the model’s procedural knowledge simple rules that can fire any time while the model is driving. This simulates the influence of distracting thoughts, as well as the effects of reduced working memory: Due to the serial nature of rule-firing in ACT-R, whenever one of the useless rules fires, it results into a slowing down of the execution of the relevant driving productions. As a result, performance will be more error prone (for related work, ritter.ist.psu.edu/acs-lab/#ACT-R/AC) [25].

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Slow Medium Fast Slow Medium Fast

Figure 4. Speed Demonstration: Lane deviation (in degrees) and total driving time (in minutes) of DUMAS in function of speed. Slow corresponds to a driving speed within the range of 15-20, medium 20-25, and fast 30-35

We compared the slow speed condition from the speed demonstration (Standard) to a condition in which the model drove at the same speed but was bothered by “obtrusive” thoughts (Worried). Figure 5 shows the result for the same set of dependent measures. The left panel shows the model predicts that average lane deviation increases when the model is worried. This confirms data generated with more complex driver models that show how a secondary task affects performance [14]. The second measure further confirms this. The right panel of Figure 5 shows how total average driving time, measured in minutes, drops significantly in the Worry condition due to an increase in the number of accidents.

A very useful aspect of the ACT-R model is that it also generates a protocol of behavioral output, illustrating how separate parts of a complex behavior like driving unfold over time. Figure 6 depicts a test run of the model, starting with the “go” production and ending with a crash. For illustrative purpose, we chose a particularly short run. As you can see, the protocol indicates what behavior (steering, cruising, “thinking about the World Cup” as worry) is taking place at what time. This protocol gives insight into the behavior, in that it shows the sequence and timing of behavior and can also indicate critical points in a behavior. Furthermore, it can be compared to the behavior of human subjects as a further validation of the model, or to gain further insight into a complex behavior such as driving.

C. Subtasks in human-robot interaction.

Even though DUMAS is still in its beginning stages and more work needs to be done, it already illustrates many of the issues that a theory of human-robot interface use will have to face. More specifically, it allows identifying a set of subtasks that appear relevant to human-robot interface use. What are these?

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Standard Worried StandardWorried

Figure 5: Lane deviation (in degrees) and total driving time (in minutes) of DUMAS in the Standard and Worried condition.

Figure 6. Protocol generated by DUMAS during a run in the multi-tasking demonstration.

1. Visual orientation: Visual input is undoubtedly the most important source of information in driving [26]. Nevertheless, the human visual system seems badly equipped for a task like driving: We only see sharply in a small center of the visual field; acuity drops significantly towards the periphery. As a result, eye movement, in the form of saccades, is needed to construct an integrated field of vision for larger scenes. To accomplish this, a driver needs a theory of where to look, and what features are important in the visual field.

Time 0.000: Go Selected**********************GO!!********************** Time 0.050: Go Fired Time 0.050: Perceive-Environment Selected Time 0.100: Perceive-Environment Fired Time 0.100: Decide-Action Selected Time 0.150: Decide-Action Fired Time 0.150: Steer-Right Selected. . . GOING TO THE RIGHT>>>>>>>>>>>>>>>> Time 0.350: Steer-Right Fired Time 0.350: Perceive-Environment Selected Time 0.400: Perceive-Environment Fired Time 0.400: Decide-Action Selected Time 0.450: Decide-Action Fired Time 0.450: Cruising Selected CRUISING Time 0.500: Cruising Fired

Time 0.500: Perceive-Environment Selected

Time 0.550: Perceive-Environment Fired Time 0.550: Decide-Action Selected Time 0.600: Decide-Action Fired Time 0.600: Steer-Left Selected<<<<<<<<<<<<<<<GOING TO THE LEFT Time 0.650: Steer-Left Fired Tim .0.650: Perceive-Environment Selected Time 0.700: Perceive-Environment Fired Time 0.700: Decide-Action Selected Time 0.750: Decide-Action Fired Time 0.750: Thinking about wc Selected “Thinking about the World Cup” Time 0.800: Thinking about wc Fired Time 0.800: Perceive-Environment Selected Time 0.850: Perceive-Environment Fired Time 0.850: Decide-Action Selected Time 0.900: Decide-Action Fired Time 0.900: Cruising Selected

CRUISING…

Time 2.400: Cruising FiredTime 2.400: Perceive-Environment selectedTime 2.450: Perceive-Environment FiredTime 2.450: Crashing Selected *********************CRASH***************

<<<Writing data to data.txt>>>>>> Time 2.500: Crashing Fired

The domain of visual orientation is probably the place where collaboration between human and robot will be most intense. Robots continue to be poor at high-level perceptual functions, like object recognition and situation assessment [27], which means the human operator will still play an important role [28]. However, USR applications have illustrated that it is often not an easy task for an operator to infer complex features from certain environments (e.g. hot spots, cavities, and voids). For instance, when maneuvering through a small and dark shaft, it is difficult to discern any features. As a result, it becomes hard to perform certain tasks like identifying victims [3]. Furthermore, the human operator can remove ambiguities that arise from the limited visual capabilities of robots, by providing information that enables the visual system to adapt to the situation at hand [29].

2. Speed control and steering: Based on information coming from the visual system, the model has to decide which speed would be appropriate. This means the user model has to have a theory that determines optimal speed in a given situation. Once the optimal speed is determined, motor procedures have to be performed to manipulate the appropriate controls. So the process of controlling speed will be determined to great extent by the constraints of other parts of the system, including control by the operator.

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A constraint that typically arises in tele-operated navigation is that of communications time lag, which occurs often as a result of navigating quickly. Our speed and multi-tasking demonstrations already indicated how important it is to keep time intervals between updating moments as short as possible. In USR applications, the time that passes between operating the controls on an interface and the robot actually reacting, creates an additional communications time lag. The impact of this additional constraint can be routinely added to our system. This model can start to help quantify tradeoffs between speed and accuracy.

The occurrence of course corrections is another interesting and unexpected parallel between our simple driving game demo and USR applications. In the driving game, situations would occur in which the system would “overreact” to a change in the environment, usually a curve, and would brake to a complete halt. Subsequently, the system would go in a recurring sequence of over-corrections and the run had to be either terminated or the model had to be helped by the human simulator. The same occurs in USR applications when the operator overshoots a desired location, sending the robot into the same type of “repetitive cycle of over-corrections” [3]. This is a known problem in human operators controls [30] [31]. It is thus pleasing and useful to see a simulated user exhibit the same maladaptive behavior.

3. Multi-tasking: Most process models of driving use very efficient and continuous processes to perceive the environment and control the car [32]. In contrast, our implementation uses a discrete updating mechanism, reflecting the discrete nature of the ACT-R model in which each step takes a fixed amount of time. This has important consequences. First, our model does not produce optimal behavior but rather aims at simulating human behavior. Specifically, its performance is determined by the speed and accuracy with which it reacts and adapts to the environment. Second, our model allows assessing the influence of a secondary task, in other words, the effects of multi-tasking (see also [14]). If the model has to divide its attention between two tasks, each of those tasks will suffer. Specifically, the time between updating moments might increase, leading to poorer performance.

Our system allows exploring the relationship between the attentional constraints of the human operator, which are captured by the cognitive model, and the operated object, be it a simulated car or robot. As such, it is perfectly suited to explore the operation of multiple robots through one user interface. It becomes possible to map the resources (attention, visual and motor capabilities) of the human operator to the performance of the robots in real-time. Our system can indicate in a quantative way, which adjustments to the interface would lead to better performance. As such, it could also indicate at what point robot autonomy could effectively compensate for human operator constraints.

Some aspects of human-robot interaction have not yet been directly addressed in the driving game, but are important enough to be included in future simulations. These are:

4. Navigation: A problem that arises with direct interfaces is that they provide poor contextual cues, which leads to less situation awareness. Using our system, it is

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possible to investigate how map building by the user is related to aspects of the user interface and characteristics of the user (expert, novice, high of low working memory capability and so on).

5. The influence of the user interface on performance: In human-robot interactions, the interface plays a secondary role. The user usually aims at completing one or more primary tasks and uses the interface to achieve her goals. The present approach is ideally suited to explore how changes in the interface will affect performance of the user on the primary tasks.

6. The level of expertise of the user. The time to learn an USR is an important factor affecting their uptake [33]. By varying the knowledge of the task in the cognitive model, one could vary the degree of skill or expertise of the user and see how this affects performance. It affects the design of the interface as well, as one would like experts to have great flexibility and control, while novices should be guarded from making large and costly mistakes. By using the current system, one could explore the relationship between user interface design and level of performance in a direct and quantative way.

IV. GENERAL DISCUSSION: WHAT THE MODEL TELLS US ABOUT HUMAN-ROBOT INTERFACES

Our model that interacts with a simulated USR-type task provides several suggestions about what makes human-robot interfaces difficult to use. These implications arise from each aspect of the model. Several problems in perception and eye-hand coordination arise from the simulated eye and hand. Addition problems can be noted from the cognitive model of this task. The cognitive modeling language, as it implements a unified theory of cognition, makes several further suggestions.

The cognitive model of the USR tasks predicts that USR tasks are difficult because they contain several difficult subtasks, and because the tasks interact. The model currently has to do several tasks at once, driving, noticing objects, and so on. These tasks alone are not difficult, but they interact with each other, competing to use the same resources, rule firings and buffer contents. If these tasks were in different modalities, they would degrade each other’s performance less. For example, having the model notice trees by saying something would be less disruptive then clicking on them.

The simulated eye of the model, based on Segman [8] explains where several difficulties for users come from. Segman was able to work fairly well on its early tasks, as its authors noted explicitly, because the computer screen is a relatively benign environment for vision. Edges are crisp, color and shapes are not ambiguous, and there are a limited number of object types. Environments for USR are basically none of these. The control screens of these systems are easy to see, but the addition of a forward looking (indeed any perspective) video display adds further complexity to the vision processing. Other reports agree that humans have problems understanding the video displays on USR interfaces [3].

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In addition to including a harder vision problem because of more ambiguous stimuli, the video displays are also noisier and of poorer quality than either vision (the display) or the controls. These effects may cause disproportionate problems for the model than for the human as humans have better vision systems, but humans too have problems with recognizing objects when the display is noisy.

Models will have some difficulty with vision processing of the video signal for some time. These difficulties are also explored in the general computer vision community. The problems that arise out of trying to understand the image like a human would suggests that there are many more problems to be solved, and that human have difficulties with these displays as well. This may be a place for computers to help users. Work on augmented reality would have a useful role here. Some of the earliest work in psychology showed that object naming was more difficult than word recognition. Screens that described or labeled ambiguous objects would help the model and are likely to help humans in this area. Such a system could help by holding visual state (going up stairs, on the third floor). These systems could also help by numbering ambiguous objects. Videos of workers at the World Trade Center search and rescue effort (available from www.crasar.org) suggest that it would be useful to have objects numbered for discussion by the operators.

The model currently cannot recognize emergent features. For example, trees that make up a line. This skill appears to be an interaction between vision and cognition. It is not straightforward to include it in a model. Similar inferential skills are necessary in USR to recognize implications such as hot walls, and unnatural positions of objects. People have difficulty in doing it, for recognizing the implications of objects in perception appears to be an important component of expertise [34].

These vision problems are compounded when considering multiple robots controlled by a single operator. It may be quite useful for computer vision algorithms to preprocess what it can, and then providing a verbal description of the robot's progress ("going through woods"). A military commander might like to see what their subordinates are doing occasionally, but if they saw everything, they would be overwhelmed. Verbal reports in that case help manage attention and reduce cognitive demands.

As the task increases in complexity, the model will have to have mental maps of the world, which is difficult for people to create without external memory aids. Unless the operator has a clipboard, paper, and pen with them, they have to hold the mental map of the world in their head. Providing support for world views in the interface would help users navigate their world.

The model's complexity and enfolding representation offers a theoretically based prediction of situation awareness. That is, the model's mental map of the world at a point in time can be compared to the world at that point in time. It will be difficult and probably not useful to assign a number to this comparison, but qualitative summaries and full descriptions of the match and mismatch give a detailed, meaningful measure of the model's awareness of the situation. The model (its

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knowledge and strategies) and the interface it uses to run robots can be modified to improve situation awareness. What is likely to happen is that some aspects can be easily improved, but that memory decay and limits to attention are likely to hinder representing the entire world in the model. The designer will be left with working on what aspects of the situation should be highlighted for the model and thus for the user.

There are difficulties specifying all the tasks that users would do with a USR robot. Previous reviews [3] provides a short list of tasks, including navigation and noticing. The current set of tasks suggests that the operators, in addition to their routine tasks, are also doing many novel tasks. Generally, performing tasks that require problem solving requires more expertise and is more error prone than well-practiced behavior. These effects may be due to the lack of practice with USR interfaces, but they may also indicate a fundamental effect of the domain. This effect suggests that models and human operators should practice doing the simple tasks to increase resources available for more complicated tasks, and that they should practice more complicated tasks to support transfer of skills between the complex tasks [35].

The model we presented and the cognitive modeling architecture it is created in, ACT-R, make several predictions about why this task is difficult for humans. We note a few here.

Learning how to use an HR interface is currently important for the acceptance of robots in urban search and rescue [3]. USR robots are currently just another tool, with restrictions on training time. If their use becomes more pervasive or specialists emerge to use USR robots like dogs are used in USR, then this may change.

Learning in the ACT-R theory, as presented by Anderson is sensitive to several factors that are usually absent in human-robot interfaces or have bad values. Feedback, its quality and amount, may be the most important factor. HRI interfaces often provide poor feedback as to the robot's location and the effects of commands. While the current model does not learn, it will have difficulties learning when commands do not always work in a uniform manner, the interpretation of the actions is hindered by imperfect perception, and when the feedback is not provided. The feedback is also often delayed, which hinders learning.

The nature of a mixed initiative interface can make it harder to use. There are more aspects of the state to keep track of, including who (agent or human) has which tasks, the state of progress of the other agent, and then context switching and communication. Models created in the ACT-R architecture can do these tasks, but the models predict that these activities will take time, attention, and working memory.

Finally, the models to use robots interact in a less direct way than models that use more typical interfaces. The models have to keep a distal representation separate from the interface. They are representing a separate world that is not the interface,

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but built from the interface. More direct interfaces can rely on the interface to hold and be the state of their world [4].

CONCLUSIONS

Based on the results of this model we can see several general lessons for the area of urban search and rescue robots and the design of their interfaces in particular.

Models can use HRI interfacesThe model presented here shows that models of users can already provide insights and summaries useful for creating better human-robot interfaces. The various levels of the model provide suggestions for improving the interface we studied, and by analogy provides suggestions for many USR robot interfaces.

The model's parameters can be varied to represent individual difference in the operator, such as working memory capacity and knowledge. The impact of these differences on performance can be examined, as shown in figures 4 and 5. These results suggest that the speed of the robot can influence its drivability, with increased speed not always leading to better performance. Differences in the interface can also be examined.

Massive application and reuse is becoming possible

Our model is not complete and is not a perfect user in many ways. It is, however, uniquely positioned to rapidly improve the scope of behavior it can represent as well as being applied to further interfaces.

Having the model interact with an off-the-shelf interface based on reading a bitmap and generateing input events directly means that the model can now start to be applied to virtually any human-robot interface. Interfaces that run under the Windows operating system can be examined directly. Interfaces that run under the Macintosh system can be examined with the Windows to Macintosh display tool VNC. Interfaces that run under the X-windows system can be examined using the Xceed Windows utility for displaying X-Windows generated displays under Windows. As noted above, there are limitations in fonts and object recognition, but these are now approachable problems.Creating the model within a cognitive architecture provides an approach for including more aspects of behavior quickly as well as providing several further advantages when creating such a large model. There are many people creating models of human behavior using the ACT-R architecture (see act.psy.cmu.edu/papers.html for a list). Some of these models are of behaviors not of interest or of use for modeling a user of HR interfaces. There are enough models, however, that we were able to start to build upon existing models rather than create our model entirely from scratch. This is theoretically pleasing because it offers an additional audience for these models, as well as other users and scientists to test the user model in formal and informal ways.

There are several models that we can already point to as being candidates for directly extending our model. Candidates include St. Amant's model of autonomous

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exploration of interfaces [8], Ritter's model of telephone dialing [36], and Salvucci's model of telephone dialing while driving [14]. St. Amant's model of interface exploration may be extendible to include victim search in the environment, a common task in for urban search and rescue robots [3].

Working within a common cognitive modeling architecture provides the resources of the architecture for people interested in understanding the model. In the case of ACT-R, this includes an online tutorial, summer schools, programming interfaces, a mailing list to get help with technical problems, and a manual.

The model predicts that human-robot interfaces are sensitive to many factors, including graphics, the processing speed of the human operator relative to the robot they are manipulating, the knowledge and processing in the robot and user, and the user's eye-hand coordination. Improving an interface that relies on this many factors is difficult without tools to help keep track of these factors and their relationships. Keeping track of these factors in complex environments, such as operating multiple robots, understanding how their control structures will be robust to perturbations, and predicting how many robots an operator can easily manipulate, will be particularly difficult.

To a certain extent, this knowledge of users and of how to support users with interfaces can be passed to designer by having them reading books on Human-Computer Interaction. This approach is useful, and model users like the one presented here can also help by providing a system level summary of users that will be interesting and informative to HRI designers.

The model explored here showed that it is not an artifact or coincidence that current human-robot interfaces are difficult to use. Our theory of HRI use, DUMAS, suggests these interfaces are difficult for all kinds of reasons. The difficulties range from the perceptual issues that must be addressed, to the relatively high-level cognition and problem solving in new situations that must be supported, as well as the relatively large amount of knowledge that is required. The model of vision that interprets the bitmap makes direct suggestions that a reason the task is difficult is because the vision recognition problem of the real world bitmaps is difficult. This result suggests that better display hardware and augmented reality particularly would help make HR interfaces easier to use. As these user models become easier to create they will be able to more routinely provide feedback directly to interface designers. In the meantime, example models like this can summarize behavior with human-robot interfaces, noting what makes human-robot interfaces difficult to use so that designers can study these problems and avoid them.

ACKNOWLEDGMENTS

This work was sponsored by the Space and Naval Warfare Systems Center San Diego, grant number N66001-1047-411F. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be assumed.

DIRK VAN ROOY is a post-doctoral researcher at the new interdisciplinary School of Information Sciences and Technology at Penn State. He earned his PhD, MS and BS in Computational

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Psychology from the Free University of Brussels.

FRANK E. RITTER (M '82) helped start the new interdisciplinary School of Information Sciences and Technology at Penn State. He earned his PhD in AI and Psychology and a MS in psychology from CMU, and BSEE from the University of Illinois/Urbana. He is on the editorial board of Human Factors and the steering committee of the Society for the AI and the Simulation of Behavior.

ROBERT ST. AMANT is an associate professor in the Computer Science Department at North Carolina State University. He co-directs the IMG Lab, which performs research in the areas of intelligent user interfaces, multimedia, and graphics. He earned a Ph.D. in computer science in 1996 from the University of Massachusetts, Amherst, and a B.S. in electrical engineering and computer science from the Johns Hopkins University in 1985.

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