using a simulated user to explore human robot...

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 model 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 does a secondary task, analogous to many HRI tasks. 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

Pass to Isaac, Rob, Rick Sherry, Robin Murphy, Salvucci, Woods, Clayton Lewis and Freed

Pass to “Nottingham guy” upon publication.

document.doc 5/18/2023 - 3:14 PM

Using a simulated user to explore human robot interfaces

D. VAN ROOY, FRANK E. RITTER Member, IEEE, and R. ST.-AMANT, Member, IEEE

Simuser to explore HRI

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

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].

There exists a large number of communication channels between human operator and robot that are

typically classified into verbal and non-verbal [3]. Lately, a great deal of interest has been directed towards

the development of multi-modal interfaces that combine several modalities such as natural-language based

interfaces, virtual reality based displays [4], emotive computing [5], and gesture recognition [6]. These

interfaces share many functions, among which reducing time to do a task, helping avoid errors in behavior,

and improving and supporting trust of the human operators in the system [7].

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 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 [8]. 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 [8]. 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].

In many ways, USR can be viewed as a form of end-user programming: A user, that is not the programmer

of the software being used, is trying to get a task done. However, key aspects of end user programming are

missing from many human-robot systems. In end-user programming, the generality of full-fledged

programming languages is typically traded for an applied extension language, which allows the user to

change the actions or interface of a system. Good examples of this are spreadsheet users that write

formulas and macros, the programming extensions for AutoCad (in Lisp) and for Microsoft Word (in

Visual Basic). Complex user interfaces like Windows use a limited set of high level principles, such as

metaphor (a desktop with folders and recycle bins), consistency, and direct manipulation, from which

detailed rules are derived.

There are several reasons why these principles from end-user programming 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 build as often as regular software and when they are built, it is usually not by people trained in HCI.

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 [9-11]. We have developed

a system consisting of the cognitive architecture ACT-R [12] and a simulated eyes and hands suite called

Segman [13] that can be applied to virtually any type of interface running on any operating system. We

will begin by describing the parts that make up the system and then a demonstration. Subsequently, we’ll

discuss how this system can be applied as a simulated user to explore human-robot interaction, leading to

explanations of user’s behavior and evaluation of interfaces.

A. The ACT-R architecture

The ACT-R architecture integrates theories of cognition [12], visual attention [14], and motor movement

([15]. It has been applied successfully to higher-level cognition phenomena, such as modeling scientific

reasoning [16], differences in working memory [17], and skill acquisition [18] to name but a few. Recently

it has been applied successfully to a number of HCI issues [19] [20] [11].

ACT-R makes a distinction between two types of long-term knowledge, declarative and procedural.

Declarative knowledge is factual and holds information like “2 + 2 = 3”. 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) and overt action (mouse-click, moving attention) has latencies associated with them that are based

on psychological theories and data. For 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., [21]). 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



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.

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 all 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 [20] 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 (www.csc.ncsu.edu/faculty/stamant/cognitive-

modeling.html).

As Figure 1 shows, Segman [13] 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



http://www.csc.ncsu.edu/faculty/stamant/cognitive-modeling.html

http://www.csc.ncsu.edu/faculty/stamant/cognitive-modeling.html

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 user-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 dum-AS (pronounced [doo ‘maa]) [22],

which stands for driver user model - ACT-R & Segman. Dum-AS drives a car in a Java-implemented

game, which can be downloaded from the internet at 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. Most importantly, it shares both surface and deep

similarities with human-robot interface use. On the surface level, it uses a first-person view as task

perspective and the environment changes dynamically in response to the actions of the user and task

environment, which is also the case for many human-robot systems. On a deeper level, driving behavior is

a prototypical example of real-time, interactive decision making in an interactive environment [23] [19].

The simulation we are using is comparable to many robot applications in that it relies heavily on

perceptual-motor skills, and involves decision-making under time pressure and interacting with a

dynamically changing environment. Furthermore, the driving game represents a simplified driving

environment, which corresponds sufficiently to real-life driving but is nevertheless a controlled setting.

Because its source code is extensible, we can manipulate aspects of the environment (e.g., slow or fast

driving) and add an interface whose features can be varied (e.g., bigger or smaller buttons), essentially

allowing for controlled experimental manipulations. Because the code is Java, this can be done on multiple

platforms. And finally, because we did not write it, it saved us time and 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 [24]; see Bellet and Tattegrain-Veste [25] for a concise historical



http://www.theebest.com/games/3ddriver/3ddriver.shtml

overview from a cognitive ergonomics perspective.) The hierarchical risk model of van der Molen and

Botticher is a representative example of recent models [26]. 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.

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 the road, avoiding oncoming

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 [27] 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 or another. 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 [28]. However, this has not led to improved

performance in this simulation environment.

In its current form, the model has problems staying on the road due to the fact that it does not handle visual

input very well. 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.



Figure 2: Two snapshots of the driving environment.

The model still represents a rather restricted model of driving behavior. Whereas typical driving models

use more than 40 rules [23], the complete behavior of dum-AS is currently determined by only 20

productions rules. Foremost, this reflects that the production system of dum-AS 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 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,

if shows a GNU Emacs window, in which a trace of the cognitive model appears (which we discuss later).

The right top half of the figure shows the debug window of the Allegro Lisp package.



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 right corner.

The experimenter starts a run by typing (run) at the Lisp prompt in the Allegro Debug window. After that,

the model starts the game autonomously by clicking on the game window shown in the right bottom of the

figure. Note that 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. In other cases, it can commit an error and would run 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.

Speed

In the speed demonstration, dum-AS completed two sets of 10 runs, one at low and one at high speed, in

the 3D Driving Game. Because the driving environment is a two-lane road, a good way to characterize

driver performance is by looking at the ability of the model to stay on its ideal line of driving, which is

about 4 degrees to the right of the center of the road. Lane deviation is commonly used in driving studies

to measure the influence of a variety of factors such as multi-tasking and drug use [29].

In this simulation, we looked at the influence of speed on the amount of lane deviation. 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 this condition: Because

more distance passes between two steering adjustments, the chance of accidents also increases. In the Slow

condition, dum-AS only had 3 accidents, compared to 7 and 10 in the Medium and Fast conditions

respectively.



0

3

6

9

12

15

Slow Medium Fast

Lane

dev

iatio

n

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 averts 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 this, we added useless knowledge to the

system designed to interfere with driving. Specifically, we added to the model’s procedural knowledge

some 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).

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



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

Slow Medium Fast Slow Medium Fast

Standard Worried Standard Worried

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 right 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 . The second measure further confirms

this. The left 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 5 illustrates this

for the multi-tasking demonstration: It 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”) is taking place at

what time.

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 0.200: Steer-Right Fired Time 0.200: Perceive-Environment Selected Time 0.250: Perceive-Environment Fired Time 0.250: Decide-Action Selected Time 0.300: Decide-Action Fired Time 0.300: 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 Fired Time 2.400: Perceive-Environment Selected Time 2.450: Perceive-Environment Fired Time 2.450: Crashing Selected *********************CRASH*************** <<<<<<<Writing data to data.txt>>>>>>

Time 2.500: Crashing Fired



Figure 6. Protocol of behavioral output generated by dum-AS during a run in the multi-tasking

demonstration.

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 behavior of human subjects

as a further validation of the model, or to gain further insight into a complex behavior such as driving.

C. A set of relevant subtasks.

Even though dum-AS 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?

1. Visual orientation: Visual input is undoubtedly the most important source of information in driving [30].

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.

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 [31], which means the human operator will still play an important role [32]. However,

USR applications have illustrated that it is often not an easy task for an operator to infer complex features

from certain environments. 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 [8].

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 [33].

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.

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” [8]. This is a known problem in human operators controls [34] [35]. It is 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 [36]. 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 [19]). 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 will

be in future simulations. These are:

4. Navigation: Our driving game has a direct interface, in that the operator directs the car using the

keyboard. This perspective is often referred to as “inside-out” driving or piloting, because the operator

feels as if she is inside the robot or vehicle and looking out, and is a common method for vehicle or robot

tele-operation [1]. 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 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. 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 or 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 they 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, if the model could notice trees by saying

something rather than by clicking on them, its performance would improve.

The simulated eye of the model, based on Segman [13] 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 [8].

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 [37].



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 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 [8]

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 [38].

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 [8]. 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 makes it harder to use. There are more aspects of the state to

keep track of, including who 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 (Wood reference please).

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, but built from the interface. More direct interfaces can rely on the

interface to hold and be the state of their world [9].

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 interfaces

The 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 passing system

function calls to generate input events 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 the 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 exploration of interfaces [13], Ritter's model of

telephone dialing [39], and Salvucci's model of telephone dialing while driving [19]. 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 [8].

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.



HRI are poor because it is difficult to pay attention to them

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 provide a system level summary of users that will be

interesting and informative to HRI designers.

HRI are poor because it is difficult for humans

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, dum-AS, 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 suggestions that

better display hardware and augmented reality 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 assured.

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 Psychology from the Free University of Brussels.

FRANK E. RITTER (M '82) has 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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