Robot Navigation for Social Tasks

Rachel Gockley

Robotics Institute

Carnegie Mellon University

Pittsburgh, PA 15213

rachelg@cs.cmu.edu

May 22, 2007

Abstract

This thesis addresses the problem of robots navigating in populated environments. Because traditionalobstacle-avoidance algorithms do not differentiate between people and other objects in the environment,this thesis argues that such methods do not produce socially acceptable results. Rather, robots must detectpeople in the environment and obey the social conventions that people use when moving around each other,such as tending to the right side of a hallway and respecting the personal space of others. By moving in ahuman-like manner, a robot will cause its actions to be easily understood and appear predictable to people,which will facilitate its ability to interact with people and thus to complete its tasks.

We are interested in general spatial social tasks, such as navigating through a crowded hallway, as wellas more cooperative tasks, such as accompanying a person side-by-side. We propose a novel frameworkfor representing such tasks as a series of navigational constraints. In particular, we argue that each of thefollowing must be considered at the navigational level: the task definition, societal conventions, and efficiencyoptimization. This thesis provides a theoretical basis for each of these categories. We propose to validatethis conceptual framework by using it to design a simple navigational algorithm that will allow a robotto move through a populated environment while observing social conventions. We will then extend thisalgorithm within the framework to allow a robot to escort a person side-by-side. Finally, we will examinehow human-like and appropriate the robot’s behavior is in controlled user studies.

Contents

1 Introduction 21.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Expected contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Related work 52.1 Human movement and interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Robot navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Social human–robot collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 General approach 103.1 Navigating with and around people . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.2 Task, Conventions, and Efficiency (TCE) framework . . . . . . . . . . . . . . . . . . . . . . . 103.3 Example 1: Vehicle caravan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.4 Example 2: Standing in line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Previous work 134.1 Affective modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.2 Encouraging physical therapy compliance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.3 A person-following robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.4 Social aspects of walking together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5 Proposed work 235.1 Navigating around people . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235.2 Side-by-side leading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.3 Side-by-side following . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.4 Robotic platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6 Timeline 31

A Rules for walk-with 32A.1 Rules for leading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32A.2 Rules for following . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

1

1 Introduction

Robots that operate in the real world encounter people on a regular basis and must react to them in someway. Traditional robot control algorithms for local obstacle avoidance treat all unexpected sensor readingsidentically: as objects that must be avoided. For a mobile robot that operates near and with people, however,the traditional methods may not always produce socially acceptable results. Even a simple convention, suchas passing oncoming people in a hallway on the right, might not be honored by a naıve obstacle avoidancealgorithm.

In contrast, this thesis proposes to develop a framework for navigational algorithms that will be usefulin many instances of physical social interaction for robots, such as navigating through crowds or standing inline. Specifically, however, we are interested in highly cooperative tasks between a person and a robot thatrequire physical interaction (movement) and are constrained by social conventions. Examples of such tasksinclude:

• accompanying a person side-by-side

• following one another while driving

• dancing

• moving furniture

Tasks of these sorts can be described by a set of rules or conventions, such as maintaining a certaindistance from a walking partner or observing the personal space of others. While these rules can and havebeen implemented as individual ad-hoc behaviors layered above a low-level navigational algorithm, we arguethat doing so fails to account for the interplay between the different conventions. Rather, we argue that theserules can be elegantly expressed as simple constraints on the robot’s navigation, in a unified manner. Forthe social tasks we are interested in, we argue that the necessary constraints are described by the followingcategories:

• Task definition. Each task may be comprised of various rules or conventions that are specific to thetask, such as maintaining a certain interpersonal distance when accompanying a person or respondingto a dance partner’s movements appropriately. In addition, many social tasks share common rules,such as obstacle avoidance.

• Societal conventions. Certain behaviors are defined purely by societal or cultural norms. Examplesof these types of conventions include which side of the hallway to walk on and the size and shape ofpersonal space.

Furthermore, we argue that the human-like behavior can be achieved by combining those constraints withefficiency considerations. People are adept at minimizing effort, and we argue that many of the conventionsassociated with these tasks can be described as an attempt to minimize effort, including both physicalexertion and communication requirements. In particular, for cooperative tasks, people attempt to minimizejoint effort, accounting for the effort required by both partners in the task.

This thesis will demonstrate that robots can be made to participate in these cooperative tasks with peopleby defining the appropriate constraints on the robot’s navigational algorithm and that these categories ofconstraints are sufficient to produce natural, predictable, and understandable robot behavior.

1.1 Motivation

We are interested in improving social human–robot interaction. As technology develops and robots becomeincreasingly prevalent, more and more robots will be operating with and around people who may not betrained in the technology. Allowing these human–robot interactions to proceed smoothly is of utmost impor-tance, particularly in light of recent psychological studies demonstrating that the increased energy demandsof inefficient social coordination continue to impact people well after the interaction is over (Finkel et al.,

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2006). Our approach to improving human–robot interaction is to create social robots, robots that behaveaccording to human social conventions and move in predictable and easily understood ways.

In particular, we wish to address the idea of a highly cooperative robot that can accompany a personside-by-side, either escorting the person or following her movements. We consider the following two scenariosas motivation for this focus.

Scenario 1 (A smart shopping cart) Alice checks out her SmartCart as she enters the grocery store.She enters her shopping list, and the cart immediately plans the optimal path through the store. The cartbegins driving autonomously through the aisles, stopping whenever it encounters an item on Alice’s list. Thecart gracefully navigates around the other shoppers and continually watches that Alice is still following. WhenAlice remembers an item she forgot to place on her list, she turns around to return to the necessary aisle. Thecart notices this, and switches into intelligent following mode. When space allows, the cart travels next toAlice in order to remain within her field of view and provide assurance that it is following her correctly, thoughit falls behind her as necessary to allow other shoppers to pass. Once Alice has retrieved her forgotten item,she pushes the cart forward, putting it back into leading mode. The cart replans its path for the remainingitems, and continues on its way.

Scenario 2 (A hospital and nursing home assistant) Bill has recently entered the assisted living re-tirement home, and he has made little effort to interact with the other residents. He has trouble finding hisway around but is too embarrassed to ask for help. Noticing his withdrawal from other people, the home’sstaff sends the robotic assistant to his room to ask him if he would like to visit with other residents. Billagrees, and the robot leads him to the common social room, where he is able to interact with others. Whenhe grows tired, the robot gently leads him back to his room.

In both scenarios, the robot must navigate with and around people who not only are not trained roboticistsbut also may have very little familiarity with technology in general. In order for the robot’s behavior to besuccessful, it must move in a way that is predictable and easily understandable to such people.

1.2 Hypothesis

This thesis argues that we can model social movement behaviors, such as observing personal space or ac-companying a person side-by-side, as navigational constraints. That is, we hypothesize that the proposedframework can be used to produce human-like behaviors in social robots. Furthermore, this thesis hy-pothesizes that adding such constraints will improve people’s perceptions of robot navigation in terms ofhuman-likeness, comfort, and personal preference for the robot’s behavior, over navigational methods thatdo not explicitly account for social conventions.

1.3 Approach

Our general approach is to study human–human interaction through both literature and direct observations,look for similarities in behaviors across different people and interaction tasks, and use design principles toapply these behaviors to human–robot interaction. We then analyze the robot’s behavior in human studiesand use the results to further inform our design. The proposed work is based on our prior work observing howpeople walk together and our studies of human–robot interaction, particularly our investigation of people’sperceptions of a person-following robot. From this work, we have developed the Task, Convention, andEfficiency (TCE) framework for representing spatial social tasks. We propose to validate this frameworkwith a simple spatial social navigational algorithm that allows a robot to navigate autonomously whileobserving social conventions, and then extend this algorithm to allow a robot to perform the cooperativetask of escorting a person side-by-side. We will evaluate both of these algorithms in human–robot interactionexperiments.

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1.4 Expected contributions

This thesis will contribute the following:

1. a framework for representing physical social tasks and conventions as constraints to robot navigation(the TCE framework);

2. a flexible obstacle avoidance algorithm designed within the TCE framework that will allow a robot tonavigate around people while observing human social conventions;

3. an implementation of a side-by-side escorting algorithm for a mobile robot, again within the TCEframework; and

4. evaluations of the human–robot interaction for both the basic navigation and the side-by-side escortingalgorithms.

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2 Related work

This thesis draws on work from many fields, including human and social psychology, robot navigation, andhuman–robot collaboration.

2.1 Human movement and interaction

When two people walk together, they coordinate their movements with each other while observing manysocial conventions, such as what distance to keep from each other and how to indicate when to turn or stop.Despite the complexity of such interpersonal coordination, very little research has been done to determineexactly what people do and what social conventions they follow (Ducourant et al., 2005; Marsh et al., 2006).

One aspect of social conventions for spatial interaction that has been widely studied is the idea of personalspace, or proxemics (Hall, 1966, 1974; Mishra, 1983; Aiello, 1987; Burgoon et al., 1989). According to Hall(1966), people maintain different culturally defined interpersonal distances from each other, depending onthe type of interaction and the relationship between the people. Specifically, Hall differentiated between fourdifferent “zones” as follows:

• Intimate: from close physical contact to about 0.5m apart

• Personal : friendly interaction at “arm’s length,” 0.5–1m

• Social : business interaction, 1–4m

• Public: speaking to a crowd, more than 4m away

For this thesis, we are particularly interested in the zone of personal space, as it is a culturally definedzone of “spatial insulation” that people maintain around themselves and others (Burgoon et al., 1989).Research has indicated that the shape of personal space is asymmetric for both approach distances (Ashtonand Shaw, 1980) and standing in line (Nakauchi and Simmons, 2000); the approximate shape of personalspace is shown in Figure 1. The exact size of personal space is not constant and differs across cultures andfamiliarity groups (Baxter, 1970; Burgess, 1983). Furthermore, the size and shape of personal space changesbased on walking speed, foreknowledge of obstacles’ movements, and other mental tasks being performedwhile walking (Gerin-Lajoie et al., 2005). Finally, the size of personal space tends to be smaller betweenpeople performing a cooperative task than a competitive one (Burgoon et al., 1989). That is, personal spaceis a cultural construct that varies across different situations.

When people interact with each other, the interaction may take the form of an intentional, focusedinteraction, such as a conversation, or it may be non-intimate, or even adversarial. Examples of non-intimatesocial interactions include how crowds gather (McPhail and Wohlstein, 1986) and what social conventions

Figure 1: The approximate shape of a person’s personal space. Frontal distance is the greatest, whilerear distance is smallest.

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are used when pedestrians pass each other, such as moving to one side, smiling, or ignoring the passingperson (Wolfinger, 1995; Patterson et al., 2002). Other social conventions used in non-intimate interactionsinclude the tendency (in America, at least) to walk on the right side of a hallway or sidewalk (Whyte,1988; Bitgood and Dukes, 2006). Most of these studies investigated these interactions from an individual’sstandpoint, rather than attempting to understand the joint actions. In contrast, Ducourant et al. (2005)studied how people attempt to break or maintain interpersonal distance when facing each other, in anadversarial situation.

Most studies of human interaction, however, have examined focused interactions, when two or morepeople intentionally engage in face-to-face conversations. For example, Kendon and Ferber (1990) studied theindividual actions people perform when greeting one another, such as waving or nodding one’s head. Studiesof people involved in conversation have shown that conversational partners unintentionally become entrainedand mimic each others’ posture (Shockley et al., 2003; Richardson et al., 2005). A large body of research hasexamined how people form and maintain common ground, the shared knowledge and suppositions betweenconversational partners (Clark and Brennan, 1991; Clark, 1996). Clark (1996) divides common ground intothree components:

• Initial common ground. Initial common ground includes all of the background facts and assumptionsmade by the participants. Knowledge of societal conventions fall into this category.

• Current state of the joint activity. Each participant has a mental representation of the state of theconversation, including what each believes the other knows.

• Public events so far. This category includes both historical events that each partner may be expectedto know as well as the history of their joint activity up to the current state.

While the idea of common ground has traditionally been applied only to conversational interactions,recent research has begun to extend the concept to any type of joint activity. In particular, successfuljoint activity requires not only common ground, but also the abilities to predict and to direct the other’sactions (Klein et al., 2005; Sebanz et al., 2006). Additional research has provided insight into how peopleautomatically predict many aspects of what others are going to do (Frith and Frith, 2006). We extendcommon ground theory to human–robot interactions, and in particular we argue that a robot’s physicalbehavior can be used to build common ground with people.

A final related area of psychology research is on efficiency, in terms of both economy of movement andalso collaborative effort. People are remarkably efficient at minimizing energy expenditure in their physicalactions (Sparrow and Newell, 1998). For example, studies conducted in malls indicate that people determinewhich side of the corridor they walk on and which direction they turn at intersections based on minimizingthe required number of steps they must take (Bitgood and Dukes, 2006). Furthermore, even in conversationpeople attempt to minimize effort, as described by the Principle of Least Collaborative Effort:

The principle of least collaborative effort : In conversation, the participants try to minimize theircollaborative effort—the work that both do from the initiation of each contribution to its mutualacceptance. (Clark and Brennan, 1991, p.226)

Klein et al. (2005) extends this concept by arguing that any time two people begin a collaborative processthey not only attempt to minimize their joint effort but also enter a “basic compact,” a tacit agreementthat they will do so. Each person can thus assume that the other will put forth the required effort tocollaborate. This idea can be applied to people who are walking together; once two people begin to walkwith the intention of walking together, each can assume that the other will continually take the necessaryactions to maintain their partnership. Gilbert (1990) terms the state of such people a “plural subject” toindicate their mutual obligations to each other.

2.2 Robot navigation

A second area of research related to this thesis is the topic of robot navigation, both for general obstacleavoidance and for specific tasks involving human interaction.

6

A mobile robot must be able to avoid obstacles in its environment, and many different algorithms forobstacle avoidance have been developed. Traditional algorithms for local obstacle avoidance include the Ar-tificial Potential Field method (Khatib, 1986) and its extension, the Vector Histogram approach (Borensteinand Koren, 1989). In both of these methods, obstacles exert a virtual force on the robot, which allows therobot to avoid collisions. However, these methods consider all obstacles as static and do not account forvehicle dynamics. Obstacle avoidance algorithms that account for vehicle dynamics, such as how quickly therobot can accelerate or decelerate, include the Dynamic Window Approach (Fox et al., 1997), the CurvatureVelocity Method (Simmons, 1996), the Lane-Curvature Method (Ko and Simmons, 1998), and LaValle andKuffner’s randomized kinodynamic planning using Rapidly-exploring Random Trees (RRTs) (1999). Algo-rithms that account for obstacles that may be moving throughout the environment include the VelocityObstacle approach (Fiorini and Shiller, 1998), Partial Motion Planning (Laugier et al., 2005), which usesRRTs to find the best partial motion to a goal within some time period, and Reflective Navigation (Kluge,2003). Finally, several approaches consider both vehicle dynamics and dynamic obstacles, including Castroet al.’s use of Velocity Obstacles within the robot’s Dynamic Window (2002), Foka and Trahanias’s predic-tive navigation (2003), and Owen and Montano’s planning in velocity space (2005). A summary of thesemethods is shown in Table 1. While any of these algorithms can be used to produce varying degrees of safeand effective obstacle avoidance, none of them explicitly account for the pre-established social conventionsthat people use when moving around each other.

A number of methods have been developed to allow robots to navigate around people in specific, typicallynon-generalizable, tasks. Some of these tasks include tending toward the right side of a hallway, particularlywhen passing people (Olivera and Simmons, 2002; Pacchierotti et al., 2005a,b), standing in line (Nakauchiand Simmons, 2000), and approaching people to join conversational groups (Althaus et al., 2004). Museumtour guide robots are often given the capability to detect and attempt to deal with people who are blockingtheir paths (Burgard et al., 1999; Thrun et al., 1999). Algorithms developed for the robot Grace allowed itto navigate a conference hall, ride an elevator, and stand in line to register for a conference (Simmons et al.,2003). Prassler et al. (2002) demonstrated a robotic wheelchair that can follow next to a person, but theirmethod does not account for social cues the human might use or allow for any social interaction. Sviestinset al. (2007) have begun investigating how a robot might adapt its speed when traveling next to a person,but have obtained mixed results even in a controlled laboratory setting. In contrast, this thesis presentsa generalized framework for integrating multiple social conventions into a robot’s behavior, thus producingmore natural and understandable robot movement.

Recent work by Sisbot et al. (2006a,b) has addressed questions similar to our own in developing a robotnavigational algorithm that considers the presence of people when planning paths through the environment.Their work considers the safety and reliability of the robot’s movement as well as its “user friendliness”—thatis, how well the robot respects human social conventions. One way in which they are addressing this ideaof “user friendliness” is through experimental studies to determine how people prefer to be approached by arobot, in terms of both distance and direction (Walters et al., 2005). In contrast, we are proposing a moregeneral framework for representing spatial social tasks, which we believe will allow our work to address awider range of social situations.

Extending any navigational algorithm to account for people requires the ability to identify and trackwhich sensor readings correspond to people. Many different ways of identifying and tracking people havebeen proposed, including using vision for color-blob tracking (Schlegel et al., 1998), using vision to track faces(Sidenbladh et al., 1999), and various laser-based methods (Castro et al., 2004; Kluge et al., 2001b; Schulzet al., 2003; Topp and Christensen, 2005; Cui et al., 2006), including our own particle-filter-based technique(Gockley et al., 2007). All of these methods have various benefits and shortcomings. All camera-basedmethods suffer when exposed to variable lighting conditions, and face-tracking methods work only when theperson is facing the robot, which is not necessarily the case in social human–robot interaction. Methodsthat use a laser rangefinder typically cannot accurately differentiate between people and other objects.Perhaps more promising are multi-sensor methods that combine information from both a camera and a laserrangefinder (Kleinehagenbrock et al., 2002; Kobilarov et al., 2006; Michalowski and Simmons, 2006). Otherresearchers have investigated the use of radio tags to track people (Bianco et al., 2003; Kanda et al., 2003),

7

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8

but these methods do not provide very accurate position information and also require instrumenting theperson with sensors. As we will discuss in Section 5.1.1, our focus is not on the sensing problem as such,and so we will rely as much as possible on these existing identification and tracking methods.

An additional aspect of understanding how a robot should navigate around people involves learningpeople’s behaviors. Several approaches have been proposed, though the typical method is to use off-linelearning techniques to build a map of “common” destination points for people in the environment, and thenuse this map to augment both on-line person-tracking and navigation (e.g. Bennewitz et al., 2003, 2005; Bruceand Gordon, 2004; Foka, 2005). While these methods have been shown to improve person-tracking, we havenot found evidence that this process is necessary for appropriate navigation around people. Furthermore,such methods are useful only in familiar environments. In contrast, we anticipate that observing socialconventions and treating people as social entities rather than obstacles will be sufficient for appropriatenavigation.

Finally, multi-robot collaborative navigation is also potentially relevant to this thesis. In particular,algorithms developed for formation-keeping (e.g. Balch and Arkin, 1998; Carpin and Parker, 2002) mayprovide insight into how a robot and a person may travel side-by-side. However, formation-keeping methodsare typically reactive, behavior-based algorithms that do not incorporate global aspects of obstacle avoidanceand path planning, which we believe is a key aspect of the social navigation problem because of how peoplebehave (see Section 5).

2.3 Social human–robot collaboration

Since we are interested in socially collaborative tasks, such as walking together side-by-side, a final relatedarea of research is the field of human–robot collaboration. However, unlike our proposed work, human–robot collaboration research has typically focused on conversational-type interaction with a stationary robot(e.g. Trafton et al., 2005; Hoffman and Breazeal, 2004; Sidner and Dzikovska, 2002). Ikeura et al. (1994)investigated cooperative object manipulation between a person and a robotic arm, and found that for thattype of physical collaboration people preferred for the robot to behave in a human-like manner.

Forming common ground between a person and a robot can also be viewed as a collaborative activity.As with the human psychological literature, much of the work on forming common ground in human–robotinteraction focuses on conversational dialog (Powers et al., 2005; Li et al., 2006). Our own prior workshows that some degree of common ground can be formed through the robot’s use of emotional expressions(Gockley et al., 2006a). Stubbs et al. (2006) iterates how problems in grounding between people and robotscan hinder human–robot collaboration, reinforcing the need for successful grounding processes. Finally,Klein et al. (2005) discusses some of the steps necessary to extend the idea of common ground theory tojoint activity between agents, such as creating agents that act predictably and signal their intentions.

Other research on socially collaborative robots includes areas such as shared attention based on gazetracking (Kozima et al., 2003; Yamato et al., 2004) and perspective-taking (Trafton et al., 2005). Whileeither of these aspects of interaction may be necessary for a fully competent social system, they are nota focus of this thesis. Somewhat more relevant may be research into behavior recognition, particularlyregarding typical behaviors in public environments (Kluge et al., 2001a). For example, a robot that istraveling side-by-side with a person may need to differentiate between behaviors such as the person stoppingto talk with a friend versus stopping because he is feeling ill. However, this thesis does not intend to addressthis particular aspect of interaction. Rather, for the current work, we expect that reacting to people insocially appropriate ways will be sufficient for navigation, as mentioned previously.

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3 General approach

Our general approach is to use psychological literature and ethnographic methodologies to understand howhuman–robot interactions should occur. That is, we begin by studying ways that people interact with eachother in particular situations. We look for commonalities across tasks in order to generate a unified frameworkfor social tasks, with which we can implement human-like robotic behaviors. Finally, we run human studiesexperiments to determine whether the robot’s behavior is human-like and how people interpret and react tothe robot’s behavior.

In particular, we are interested in spatial social tasks, that is, tasks that require the robot to navigatewith and around people. Such tasks include traveling through populated environments, such as a medicinedelivery robot in a hospital, as well as cooperative tasks, such as accompanying a person side-by-side.

3.1 Navigating with and around people

Navigating around people requires that the robot be able to detect and interpret the movements of people inthe environment. Because of existing social conventions, the robot must be able to treat people differentlythan inanimate objects. In particular, the robot must be able to:

• detect the locations of static obstacles in the environment, such as walls, chairs, doors, and so on;

• detect the current location, track the velocity, and be able to predict the future trajectory of mobileobstacles in the environment; and

• identify (with some degree of confidence) which obstacles are people, so that the robot may follow theappropriate social conventions.

The robot must navigate in a way that is predictable and easily understood by people in the environment.The robot’s ability to observe and respond to social conventions will serve to foster common ground betweenpeople and the robot, allowing people to make assumptions about the robot’s behavior and respond correctlyto the robot’s movements. This will allow both the robot and nearby people to move safely and efficientlythrough the environment.

3.2 Task, Conventions, and Efficiency (TCE) framework

This proposal argues that spatial social tasks must be implemented at the navigational level, accountingfor the task definition, societal conventions, and efficiency constraints. Preliminary evidence indicates thatthese categories are sufficient to produce natural, human-like behavior in social robots. Here, we draw fromthe theory presented in Section 2 to provide a basis for this conceptual framework.

3.2.1 Task definition

Every spatial task has a goal, such as “travel from point A to point B” or “follow one meter behind thisperson.” The components of this goal can be enumerated as a series of constraints that together definethe task. In addition, many tasks have common constraints. In particular, obstacle avoidance is almostuniversally a component of social spatial tasks, though one can imagine tasks in which other componentsof the goal do require the robot to physically contact some object (to retrieve or examine it, for example).Another component common to many social spatial tasks is the idea of maintaining a certain distance froma person, such as is the case for following behind someone, walking next to someone, or even standing inline. Other task constraints will need to be enumerated for individual tasks.

In traditional robot control architectures, obstacle avoidance is handled as a low-level navigational methodonto which complex tasks are built. However, to create human-like social behaviors, other aspects of the taskmust often influence the obstacle avoidance behavior. For example, if the task requires the robot to maintaina certain lateral distance from a person, the robot must use this constraint to determine how to jointly passan obstacle with its partner. To best integrate these different task components, then, the constraints must be

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implemented at the navigational level. For example, obstacle avoidance can be implemented by constrainingthe robot’s allowable velocities to those that will not produce a collision in some time frame. Similarly, arobot can follow a person by constraining its velocity to match that of the person. More complex tasks willrequire more complex constraints. Following a path may map to multiple constraints, such as minimizingdeviation from the current preferred heading and biasing the robot’s movements in favor of future pathsegments (e.g. anticipating turns).

3.2.2 Societal conventions

As discussed in Section 2.1, people use a variety of conventions when interacting with one another, and manyof these conventions are purely societally defined. For example, while most cultures have some concept of“personal space,” the size and shape of this space varies from culture to culture. Similarly, while Americansmay tend to walk on the right side of a hallway, many Asian cultures tend to walk on the left side. Suchconventions have no direct relevance to spatial tasks—one can travel from point A to point B on either sideof a hallway—but failing to follow them will likely confuse other people in the environment. The ability tofollow these conventions, then, is necessary to complete spatial tasks in a socially acceptable manner.

As with the task definition, societal conventions can also be expressed as navigational constraints. Per-sonal space is simply an extension of general obstacle avoidance; the robot’s velocity must be further con-strained to avoid the personal space “zones” around people in the environment. Tending to the right side ofa hallway can be represented as a constraint that values free space on the right more highly than space onthe left. Other societal conventions can be represented similarly.

Different tasks may require knowledge of different sets of conventions; a robot navigating through asparsely occupied hallway may need to know less of human behavior than a robot accompanying a personaround a crowded shopping area. We can learn what societal conventions are relevant to any particular taskthrough existing sociological literature or through ethnographic observations.

3.2.3 Efficiency optimization

Finally, we argue that all aspects of navigating in a human-like manner that are not subsumed by the taskdefinition or societal conventions fall under the category of efficiency. As we discussed, people are remarkablyefficient, from choosing walking paths that minimize metabolic energy to constructing dialog that minimizesmental effort for both the speaker and the listener. To navigate with and around people in a natural andpredictable manner, then, a robot should also attempt to minimize its required effort.

The idea of efficiency encompasses a wide range of constraints, including movement constraints, suchas the effort required by the robot to travel a certain distance, turn, accelerate, or decelerate. For socialtasks, the efficiency considerations must also include the communicative efforts of interacting with people.Finally, for cooperative tasks, the robot may also need to consider the human’s effort—that is, the robotmust attempt to optimize the joint effort between itself and its human partner, in holding with the Principleof Least Collaborative Effort discussed in Section 2.1.

3.3 Example 1: Vehicle caravan

To better understand these categories of constraints, consider the case of a vehicle caravan. The taskdefinition is that one car must lead another to some final destination. Societal conventions dictate thatthe cars must both travel on the right side of the street, stop for stop signs and red lights, obey the speedlimit, and so on. Efficiency requires that the leading car consider the following car’s effort when determiningits own actions. If the leading car approaches a traffic light as it turns yellow, then from the single car’sperspective accelerating through the light may be less effort than attempting to stop the car. However, whenconsidering the task of remaining in a caravan, the first car must consider the following car’s ability to alsopass through the light before it turns red. From an efficiency standpoint, if the second car is dynamicallyconstrained so that it is unable to accelerate fast enough but the first car travels through the intersectionregardless, then the first car must exert additional effort to wait after the light for the second car to proceed,

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or else the task will fail. In this case, joint effort of both cars would have been less if the first car had stoppedat the light.

3.4 Example 2: Standing in line

As another example, consider the more complex case of a customer checking out at a grocery store. The taskdefinition requires that he must pass by one (of many) check out counters, where he will pay for his items.To get to the counter, he must wait in line, which can be viewed in a high-level sense as efficiency—jointly,everyone will be less efficient if he attempts to cut in line—or perhaps more simply as another aspect ofthe task definition, in which the presence of other people constrain his access to the counter. Which linehe selects depends on his calculation of efficiency, which may include a trade-off between the time requiredto examine each line versus choosing the shortest line that is also the closest. How he actually positionshimself in line is based on societal conventions of personal space, which keep him from running into othersand also allow him to move forward (in pursuit of his goal of the counter) when the line moves. Because ofhis continual attempt to optimize his efficiency, this framework additionally accounts for behaviors such asswitching to a different line if the selected one is seen as moving too slowly according to his efficiency model.

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4 Previous work

We use our previous work with social robots to inform our general approach as well as provide a basis forour proposed work.

4.1 Affective modeling

One of our first studies in social human–robot interaction involved understanding how people react to a robotthat displays moods and emotions. This study utilized the Roboceptionist platform, a robot stationed neara building entrance that interacts with visitors to provide information as well as entertainment (Gockleyet al., 2005). The Roboceptionist is shown in Figure 2. This platform was advantageous due both to itsability to display a wide range of facial expressions and to its frequent and unstructured interactions withmany people over a long period of time.

4.1.1 Generative model

We developed a generative model of affect for social robots (Gockley et al., 2006b), drawing from psychologicaltheories on human interaction. In particular, we chose to model affect according to the following definitions,as categorized by Scherer (2000):

Affect. Affect is a general term relating to emotions, moods, and other such states with varying degrees ofpositivity or negativity—that is, states with valence.

Emotions. An emotion, or “emotional response,” is an immediate affective response to the evaluation ofsome event (or other stimuli) as being of major significance. While psychologists may debate whetheremotions have distinct facial responses (see e.g. Izard, 1994; Russell et al., 2003), for our purposesemotions are meaningless unless they result in some outward change in the robot, including facial,vocal, or behavioral modifications. We modeled emotions as facial expressions that are triggered bystatements made by visitors to the Roboceptionist, producing expressions such as those shown inFigure 3.

Moods. Moods are more “diffuse” affective states (Scherer, 2000) that typically do not have a single an-tecedent. They are typically of lower intensity than emotions and have fairly low variance over the

Figure 2: The Roboceptionist robot in its booth.

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Figure 3: Interpolation of emotional expressions between neutral (far left) and very angry (far right).

course of a single day. Moods may be caused by any number of things, including changes in physiolog-ical state (such as lack of sleep or illness), rapidly occurring emotional responses, or complex cognitionregarding emotional life events (Ekman, 1994; Lazarus, 1994). The Roboceptionist’s moods are trig-gered by its “life” events (as determined by the dramatic writers), and its moods both influence andare influenced by the robot’s emotions.

Attitudes. An attitude is an amalgamation of emotions experienced with a particular person (or thing),reflecting one’s relationship with that person over time. Attitudes may change due to emotions inspiredby the focus of the attitude, co-experienced emotional events, and the frequency and duration of therelationship. A person’s attitude toward a conversational partner can influence many aspects of theconversation; for example, people are less likely to express emotions in the presence of strangers (Wagnerand Smith, 1991). Attitudes are a key part of long-term relationships. In our model, attitudes areformed toward people over the course of their interactions with the robot.

Further details of the affective model, including mathematical equations and examples, can be found inGockley et al. (2006b). Currently, the robot’s affective state influences its facial expression but does nototherwise alter the robot’s behavior. More natural behaviors could be produced by using the robot’s affectto constrain its behaviors—physical or verbal—using the proposed TCE framework.

4.1.2 Interactions with a moody robot

To begin validation of the affective model, we investigated how people reacted to the robot’s displays ofemotions and moods. In particular, we changed the robot’s facial expression to display different moods(positive/happy and negative/sad) and measured how people’s interactions with the robot changed. Wemeasured how many people interacted with the robot and how long they spent typing to it. In addition,at several points during the study we conducted surveys of people who had interacted with the robot. Fullresults from the study are reported in Gockley et al. (2006a). We found that how people’s interactionpatterns changed was highly influenced by their prior familiarity with the robot. Visitors to the university,who likely had less familiarity with the robot, tended to interact with the robot more often and for longerperiods when it displayed either emotion rather than a neutral expression. In contrast, people who weremore familiar with the robot interacted more often but for shorter periods of time when the robot appearedhappy than in either other condition. This finding, combined with the results from the surveys, is consistentwith the idea that the robot’s apparent mood influences the amount of common ground people feel with therobot. For this thesis, we argue that this grounding process can be extended to include other aspects of therobot’s behavior, including movement. That is, just as the robot’s display of human-like emotions impactedgrounding, the use of social conventions in movement should also foster common ground between the robotand people.

4.2 Encouraging physical therapy compliance

Our initial work on human–robot spatial interaction addressed the use of a robot in “hands-off” (non-contact) physical therapy. We designed a user study to determine how the physical presence of a robot could

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Figure 4: A participant in the physical therapy compliance study, transferring pencils between baskets.The robot (a Pioneer 2DX) is at a social distance.

be used to encourage people to comply with therapy treatments. In this study, participants performed threerepetitive-motion arm tasks similar to ones commonly used in stroke rehabilitation: moving pencils betweenbaskets, shelving magazines, and paging through a newspaper. The robot, a Pioneer 2DX, was randomlyplaced into one of each of the following proxemics and engagement conditions:

• Proxemics. The robot attempted to maintain either a personal distance (0.5–1.0 meters) or a socialdistance (1.5–2.0 meters) from the participant, hypothesizing that people’s preferences for the robot’sdistance will vary according to their personalities.

• Engagement. Following research that indicates that people prefer and are more persuaded by agentswho mimic their movements at a short delay Bailenson and Yee (2005), we designed a “high engage-ment” behavior where the robot moved forward and back following the speed of the participant’s armmovements (with a random lag). In the “low engagement” condition, the robot remained primarilystationary, and did not follow the participant’s arm movements.

The experimental setup can be seen in Figure 4. We ran the study with 11 participants (8 male and 3female), each of whom experienced two robot conditions and one control condition with no robot present.In general, we found that participants were not always able to identify the engagement condition, butthat participants did tend to exercise longer when they perceived the robot as more engaged with them.Additionally, we found a strong inverse correlation between participant extroversion and preference for therobot to stay further away; that is, more extroverted participants were less likely to feel that the robot cametoo close to them. Further details of this study can be found in Gockley and Mataric (2006).

In this study, the robot’s task was not particularly human-like; moving forward and back according tosomeone’s arm movements is not a common social activity. Regardless, aspects of the task fit within the TCEframework. In particular, the two robot conditions, proxemics and engagement, can each be implementedas navigational constraints: proxemics constraining the robot’s distance from the person and engagementconstraining the robot’s movement to the person’s arm movements. However, as a result of this task notbeing truly social, it does not have any particular notion of efficiency. Thus, this task may be demonstrativeof the type of tasks for which a partial implementation of the TCE framework would still be beneficial.

4.3 A person-following robot

In further work on human–robot spatial interaction, we have designed and tested two different person-following behaviors for a mobile robot, as discussed in Gockley et al. (2007). The first and simplest methodis to have the robot always attempt to drive directly toward the person’s location. From general observations,

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Figure 5: The robot Grace following behind a human leader.

we suspect that this is how people most often follow other people. This method often results in the followercutting corners and generally not following in the exact footsteps of the leader. The second method, then,is to have the robot attempt to follow the exact path that the person took. While this method may notbe the most human-like method, we hypothesized that it may better match people’s expectations for amachine-like robot. For example, if a person is leading a robot somewhere, any step in the person’s pathmay be taken for reasons that the robot does not know, and thus following the person’s exact path maybe the more appropriate behavior. We have implemented both of these methods, using a laser-rangefinder-based person-tracking system similar to that of Topp and Christensen (2005), on the robot Grace, shown inFigure 5.

In both methods, the robot begins to follow a person as soon as someone is detected within 125cm ofthe robot, in a cone of ±0.5 radians. The robot then attempts to remain a constant distance (120cm, plusor minus 10cm) from the tracked person. This is achieved through a simple proportional feedback controlloop based on the error between the robot’s current distance from the person and its desired position.Additionally, the change in range error over time is used to reduce oscillations in the system. Specifically, ifthe robot is too far from the person and the range error has increased (meaning the robot is falling furtherbehind), then the velocity is increased based on the error; if the robot is too close and is getting closer, thenthe velocity is similarly decreased. The robot stops if the distance to the person drops below 90cm. Therobot’s maximum velocity is capped at 70cm/s, due to safety concerns.

Currently, the distance at which the robot tries to follow is held constant, and is designed to keep therobot just outside of one’s personal space. As several studies have found, the appropriate distance may varyaccording to an individual’s personality traits (Walters et al., 2005; Gockley and Mataric, 2006). We havenot tested the person-following with different distances at this time.

The two person-following methods differ in how they select the robot’s direction of travel. These differ-ences, as well as social aspects of the robot’s behavior, are discussed in the following sections.

4.3.1 Direction-following

In this method of following a person, the robot simply attempts to drive in the direction of the trackedperson’s current position. This is combined with the underlying obstacle avoidance control system by settingthese goal directions using the Curvature-Velocity Method (CVM) developed by Simmons (1996). With thismethod, the robot is able to follow the person through doorways and around corners without collisions.

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Person-following and most obstacle avoidance methods are fundamentally at odds, since following aperson requires the robot to drive straight toward something that would normally be interpreted as anobstacle. To convince the Curvature-Velocity method to follow a person, we weighted the CVM parametersto strongly favor the goal direction over the preferred distance from obstacles and the preferred maximumspeed. That is, the robot will favor going slowly close to obstacles (such as the person) as long as its headingis correct. However, this method is not optimal, as changing the parameters in this way also results inhigher chances of collisions with static obstacles, such as walls. In contrast, this thesis proposes to providea better integration of the task (following closely behind a person) and its inherent social conventions withthe low-level navigational algorithm.

4.3.2 Path-following

In this more sophisticated approach, the robot attempts to follow the path that the person took as closelyas possible, such as switching to the opposite side of the hallway at a certain location and driving aroundcorners with the same curvature as the person’s travel. Path-following is achieved in much the same wayas direction-following, except that the robot’s goal direction is chosen according to the Pure Pursuit path-following algorithm (Coulter, 1992). At each tracker cycle, the person’s location is stored, building a historyof the person’s path. The robot’s goal point is selected as the point at which the person was at the desireddistance from their current position (that is, 120cm prior on the person’s path). As with direction-following,the CVM method is used to integrate obstacle avoidance with the person-following behavior.

In addition, the robot’s goal direction is constrained such that the robot will never intentionally turn toa point at which it can no longer track the person. This is necessary because the robot does not have afull 360-degree sensor coverage, but means that the robot may not always follow the person’s exact path,particularly if the person walks in a tight circle around the robot. Such a constraint is unnecessary for thedirection-following method, since in that case the robot always turns to face the person regardless of theperson’s path.

4.3.3 Interaction and error recovery

To make the robot’s behavior more social and human-like, it periodically offers suggestions and encourage-ment, using synthesized computer speech. Additionally, the robot will verbally notify the person if he orshe is walking too quickly for the robot to follow, as an able-bodied person can easily out-pace the robot.Importantly, this vocalization is also used to aid error recovery. If the laser tracker loses the target person,the robot immediately stops and provides verbal notification. When this occurs, the robot also reverts to itsinitial state of waiting to detect a person immediately in front of the laser. Target reacquisition is usuallyachieved by the person taking a few steps back toward the robot.

4.3.4 Performance

The two person-following algorithms differ most noticeably when turning corners; the direction-followingapproach results in the robot rounding corners much more so than the person did, whereas the robotexplicitly attempts to follow the same curvature as the person when using the path-following approach. Thisdistinction can be seen in Figure 6.

We analyzed two aspects of the person-following algorithms: how well the robot was able to follow aperson and how observers interpreted the robot’s behaviors. To quantitatively compare the functionality ofthe two algorithms, each behavior was tested repeatedly at varying speeds. No significant difference wasfound between the two behaviors in terms of either the distance between the robot and person or the timebetween tracker failures.

We performed a pilot study to explore whether people subjectively prefer one person-following methodover another. Participants in the study observed the robot as it followed behind the experimenter andanswered a short questionnaire regarding each of the two person-following algorithms. Due to the within-subjects nature of this study, we analyzed the survey responses using paired t-tests across trials. Average

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Figure 6: Paths of the person and the robot around corners, for each of the two approaches. Therobot drastically cuts corners when not following the person’s exact path. Note that each path shownis roughly 15m in length.

Following Behavior PairedQuestion Direction Path t

Met expectations (not 5.0 3.7 -4.33*at all—very much) (0.94) (0.95)Natural (not at all— 4.0 2.9 -3.97*human-like) (1.15) (0.88)Following distance 3.0 2.9 −0.43(too close—too far) (0.94) (1.29)Stopping distance 4.9 5.4 1.86(too close—too far) (1.20) (1.71)

Table 2: Average responses to the survey questions, with standard deviations given in parentheses.All questions were asked on scales of 1–7. N = 10. *significant at p < 0.01

responses and t-values for each question are given in Table 2.Although the two behaviors are very similar, participants noticed their differences. Participants were

asked to rate the robot’s behavior according to whether it met their expectations (“not at all” (1)—“verymuch” (7)) and how natural the behavior was (“not at all” (1)—“human-like” (7)). As shown in Table 2,participants rated the robot’s behavior as significantly more natural and human-like in the direction-followingcondition. In addition, participants felt that the direction-following robot behaved more according to theirexpectations. Notably, none of the participants rated the path-following behavior as better than the direction-following behavior on either of these questions. Furthermore, the answers to these two questions were highlycorrelated (r = 0.80, p < 0.0001), indicating that participants expected the robot’s behavior to be human-like, despite the robot’s non-anthropomorphic physical shape.

Participants were also asked whether the robot followed and stopped at appropriate distances fromthe experimenter. They rated the distances on a scale of 1 (“too far away”) to 7 (“too close”). Overall,participants felt that the robot stayed a little too far away from the experimenter while moving (overallmean 2.95, SD 1.10), but stopped at an appropriate distance (overall mean 5.15, SD 1.46). There were nosignificant differences in participants’ answers across the two person-following behaviors, which parallels thequantitative functionality discussed above.

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4.3.5 Summary

Quantitatively, the two methods of person-following are equivalent; the behaviors do not differ in lasertracking performance, and both allow the robot to follow smoothly behind a person. The primary differencebetween the two behaviors occurs at corners, when the direction-following behavior causes the robot to curvemuch more gently than the person, given sufficient space to do so. If the following occurs in narrow corridors,the differences between the behaviors lessens, as obstacle avoidance constrains the robot’s movement.

Qualitatively, however, people indicate that the direction-following behavior is significantly more human-like and more closely matches their expectations than when the robot follows the person’s path. Severalparticipants commented that, when performing path-following, the robot did not appear to react “quicklyenough” to the person’s turns (since the robot turned at the location where the person turned, rather thanat the same time as the person), which may help explain this finding.

To date, we have performed only the small pilot study as described above. Obviously, many caveatsapply to this sort of study, as the participants were all familiar with robots and most likely had verydifferent expectations of robotic behavior than non-roboticists. However, while the participant populationmay have influenced the exact values of the survey questions, we expect that the relative differences betweenthe different robot behaviors would be similar across other populations, as well. A further shortcoming ofthis study is that it analyzed only people’s third-person observations of the robot’s behaviors, and thus theresults may not capture the full spectrum of people’s preferences. However, while people’s in situ experiencesof a robot’s behavior are clearly valuable, such testing is difficult to perform and evaluate when the robot’sbehavior occurs strictly behind—and thus out of sight of—the person.

As mentioned, our implementation of person-following behaviors was hindered by the use of a standardobstacle avoidance algorithm, the Curvature-Velocity Method. While some of the difficulties we encounteredwould be remedied by the use of a navigational method that accounts for dynamic obstacles (see Table 1),we argue that person-following behavior could be more human-like as well as more easily implemented usingthe TCE framework. In particular, the robot’s speed and direction could be constrained to match that of theperson (task rule) while still respecting the person’s personal space (social convention). Adding an efficiencyconstraint to keep the robot’s overall movements to a minimum would result in a “direction-following” typeof behavior. (Producing the “path-following” behavior would require additional task rules to constrain therobot’s movement to the person’s prior path.)

4.4 Social aspects of walking together

As discussed in Section 2.1, little work has been done to date to understand the social conventions usedwhen two people walk together. To provide a basis for our proposed work, we performed an observationalstudy of how older adults in a local retirement community walk together, using ethnographic methodologiesborrowed from social anthropology. These results can also be found in Gockley (2007).

4.4.1 Procedure

Observations took place at a local retirement community. Investigators used an ethnographic approach thatinvolved making observations as unobtrusively as possible while seated, standing, or walking within 20 feetof participants (investigators were somewhat conspicuous by virtue of their younger age in comparison to thecommunity residents). Genders of the participants and observation locations were documented. Observationswere made of pairs of people regarding:

1. what route they took, including stops;

2. the relative ages of the walking companions (e.g. two older adults, one older adult and one staff person,etc.);

3. how the companions positioned themselves relative to each other;

4. whether either person had any obvious disabilities, including walker use;

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EscortingLeader Follower Count Typical interpersonal distances

Side-to-side Front-to-backResident Resident 4 1–2’ ≤ 1’ *Resident Non-resident 1 ≤ 1’ 3’ *

Non-resident Resident 14 ≤ 1’ ≤ 1’ *Non-resident Non-resident 5 1–2’ varied †

SocialPairing Count Typical interpersonal distances

Side-to-side Front-to-backBoth residents 15 ≤ 1’ 0’

Both non-residents 7 1–2’ varied †

Resident with non-resident 8 0–2’ varied †

Table 3: Number of walking pairs observed, separated by situation (escorting or social), with typicalinterpersonal distances for each situation. Total pairs observed: 54. *Directly side-by-side or leaderin front. †Partners within each pair did not maintain a consistent distance.

5. what each person was holding or carrying;

6. whether one person was leading or escorting the other (if so, who was in which role); and

7. the amount of social interaction, both of the two walkers and of any interactions with people outsideof the pair.

Residents and staff received prior notice of the study through the community’s weekly newsletter, andthe experimenter willingly explained the nature of the study when requested during observations. To protectresidents’ privacy, no personally identifying information was collected and no photographs were taken.

4.4.2 Results

Observations were performed in three-hour blocks on 4 days, for a total of 12 hours. Data was collected on54 pairs of people. The situational breakdown of people can be seen in Table 3.

Escorting behaviors. We observed several behaviors specific to escorting situations, including gestures,physical contact, and body movements to indicate direction.

• Gestures and physical contact. In escorting situations, the leader often used gestures or physicalcontact to indicate the intended direction. We observed five instances of the leader pointing towarda destination, and four instances of the leader using physical contact—a hand on the follower’s arm,shoulder, or back—to direct the follower.

• Body movements. Intuitively, we suspect that leaders use movement into or out of their partner’spersonal space in order to indicate turns along the path. Unfortunately, these movements are subtleand difficult to detect in this sort of observational study. We observed several instances, typicallyinvolving a non-resident leading a resident, where the leader appeared to speed up on outside turnsand slow down on inside turns, allowing the follower to maintain a constant speed. In addition, weobserved one instance where such body movements failed to properly convey a turn; the leader beganto turn a corner by moving away from the follower, but the follower did not immediately correct hermovement. Rather, once the pair had separated to about 1.5m between them, the leader turned to theother, gestured, and said, “This way.” That is, a failure in leading via body movements was correctedwith a gesture and spoken command.

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Interpersonal distances. Table 3 lists the typical distances maintained between walking partners. Thesedistances were estimated by the observers from a distance (typically from across the room), and thus shouldnot be considered absolute measurements. All distances (both side-to-side and front-to-back) between com-panions were highly variable—not just across different pairs of people, but also within individual pairs asthey walked. However, we can note that pairs consisting of two residents walking socially or of a non-residentand a resident in an escorting situation tended to maintain much closer side-to-side distance (0.5m or less)than most other types of pairs. In addition, either partner’s use of a walker or cane did not appear to havean impact on their interpersonal distance.

Obstacles and bottlenecks. We observed four main behaviors when pairs encountered obstacles (e.g.another person or object in the way) or bottlenecks (narrowing of the passageway):

1. Simultaneous movement. Both partners simultaneously move to the side. This behavior was observedonly once; both partners were able-bodied and had sufficient space in the hallway to avoid the obstacle.

2. Speed increase. One partner speeds up to pass the other and proceeds first (observed 8 times). Thisbehavior occurred primarily in social accompaniment situations, and in particular occurred when onepartner was able-bodied but the other was not, in which case the able-bodied partner proceeded first.

3. Speed decrease. One partner slows down and allows the other to proceed ahead. This behavior wasobserved 12 times, in the following situations:

• When both partners were able-bodied, the partner closest to the obstacle fell behind while theother partner proceeded straight ahead.

• When a more able-bodied person was leading a less able-bodied follower, the able-bodied leaderslowed down, allowing the other to pass, and often used physical contact (such as a hand on theother’s shoulder) to continue guiding the other from behind.

• This behavior was also observed in social accompaniment situations between an able-bodied anda less able-bodied person, in which case the able-bodied person slowed down to let the other passfirst.

4. Separation. The partners pass on opposite sides of the obstacle. The retirement community’s commonroom has a central lounge area with multiple chairs and couches surrounded by several structuralcolumns. In several cases, when partners were walking together through this area, they would separateand pass on opposite sides of a column or chair before returning to side-by-side travel. This behaviorwas observed three times and only occurred in this common area around inanimate objects; no partnerswere seen separating within a restricted hallway or around a person.

Unexpected stops. We observed five instances of one partner stopping suddenly—to speak to a passerbyor to search through a bag—without the other partner’s prior knowledge. In each of these cases, the otherpartner continued on for 0.5–2.5m before stopping, then turned to face the stopped partner. Generally, theother person did not reverse direction, but rather waited in place for the first to resume walking.

Social interaction. In general, partners who were conversing with each other tended to look forward, withoccasional glances toward the other partner. However, more detailed observations (such as video coding)may be necessary to fully understand the use of gaze in such situations.

Gender differences. We are not currently able to report on gender differences due to a heavy bias towardwomen in both the residents and the staff. Of the 54 pairs observed, only 15 contained at least one malepartner, and only three of those pairs were both male.

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4.4.3 Summary

We have performed an observational study of how older adults walk together in pairs. From this study wehave been able to derive the specific conventions that people obey when walking in pairs, such as how farapart they walk from each other and how they signal where they are going. We argue that all of these rulescan be labeled as either societal conventions, task constraints, or efficiency considerations. See Appendix Afor the complete task description and constraint labeling.

An obvious question regarding this research is whether it generalizes to locations and populations otherthan this particular retirement community. Obviously, we cannot state conclusively that it does withoutfurther research. However, from casual observations, we anticipate that the conventions we listed above dogeneralize (at least to American populations). While conventions such as interpersonal distances and speedsare likely influenced by the relative ages, social status, relationship, and so on, between the two partners,these can all be modeled by additional societal constraints within the TCE framework.

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5 Proposed work

This thesis proposes to address spatial social tasks, tasks that require a robot to navigate around people. Inaddition to the TCE framework, the expected contributions of this thesis include a navigational algorithmthat allows a robot to respond to and observe spatial social conventions and an implementation of a robotthat can escort a person side-by-side in a human-like manner.

5.1 Navigating around people

We intend to design and implement a robot control algorithm that accounts for people using the TCEframework. That is, the algorithm must treat people differently than inanimate objects when performingobstacle avoidance. The algorithm must include both object tracking and navigational components.

5.1.1 Object tracking

In order to navigate safely, a robot must be able to identify the locations of obstacles in its environment.Furthermore, if a robot is to avoid obstacles in a human-like manner, it must be able to predict the trajectoriesof moving obstacles (Foka, 2005).

Currently, we have an implementation of a laser-based person-tracker that computes the approximatevelocity of each person it tracks. However, our current implementation is an independent module from theunderlying obstacle avoidance algorithm. As noted in Gockley et al. (2007), this is a sub-optimal approach,as the obstacle avoidance does not differentiate between people and objects and does not consider humanconventions such as personal space. We intend to integrate our current person-tracker into a more generalobstacle-detection algorithm, with the characteristics listed in Section 3. That is, the algorithm must beable to detect and track static as well as dynamic obstacles and must also be able to detect which sensorreadings correspond to people.

Static obstacle detection is trivial and can be easily implemented on any robot equipped with sensorssuch as sonar- or laser-based rangefinders. Unfortunately, the remaining characteristics listed are much moredifficult. Implementations exist for both mobile obstacle tracking (e.g. Castro et al., 2004; Fod et al., 2001;Vermaak et al., 2005; Jaward et al., 2006; Schulz et al., 2001) and for person identification and tracking (e.g.Montemerlo et al., 2002; Schulz et al., 2003; Kleinehagenbrock et al., 2002; Topp and Christensen, 2005), aswell as our own person-tracker mentioned previously, and we expect to combine several of these approaches.In addition, we may simplify the person-detection problem by instrumenting people in the environment,since accurate person-detection is necessary for successful robot behavior but the sensing problem is not afocus of this thesis.

5.1.2 Navigation

As discussed, we believe that physically cooperative social tasks can be fully described by the followingcomponents:

• task definition,

• societal conventions, and

• efficiency, including kinodynamic considerations.

As discussed in Section 2.2, several researchers’ prior work supports the idea of representing these taskcomponents as navigational constraints. In particular, many of the algorithms mentioned in Table 1 havedemonstrated that kinodynamic considerations, such as the acceleration and deceleration profiles of therobot, can be expressed in the form of constraints on the robot’s movement. Olivera and Simmons (2002)have shown that the societal convention of tending toward the right in hallways can also be expressed as asimple navigational constraint. We argue that the remaining task rules and social conventions can also beencoded as such constraints, as discussed below.

In order to solve this problem, we intend to address the following questions:

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Figure 7: An agent approaching an obstacle.

• How should the task be defined as constraints?

• What social conventions are needed and how should they be encoded as constraints?

• How should the different constraints be combined to determine the robot’s behavior?

Task constraints. As discussed in the General Approach (Section 3), to produce human-like behaviors,the task itself must be represented in the navigational algorithm. We propose that this can be done byencoding the task as navigational constraints. For some tasks, this encoding may be straightforward. Forexample, following behind a person can be represented as a constraint on the robot’s velocity to keep therobot within a certain distance from the person.

As we have discussed, obstacle avoidance is a component of most social tasks. To a certain extent, obstacleavoidance can be handled as simple navigational constraints, as with the Velocity Obstacle approach (Fioriniand Shiller, 1998). However, we agree with Foka (2005) in arguing that the obstacle avoidance problem cannotbe treated in a strictly local, reactive manner, if robots are to move in an efficient, human-like manner. Todemonstrate why, consider the case shown in Figure 7, where the robot is approaching an obstacle. From apurely local point of view, either of the avoidance maneuvers shown in Figure 8 could be taken. However,if the obstacle is considered in the global context of the robot’s final goal, as shown in Figure 9, one cansee that the most efficient path is for the robot to move to the right of the obstacle. Foka (2005) presentsone possible solution to the integration of local and global obstacle avoidance, called the Robot-NavigationalHierarchical Partially Observable Markov Decision Processes (RN-HPOMDP). Another approach, which webelieve may be simpler but still effective, is to plan using Rapidly-exploring Random Trees (RRTs), as inLaValle and Kuffner (1999). RRTs alone may not produce the most efficient paths, but the addition of aheuristic bias can greatly improve the path quality (Urmson and Simmons, 2003).

Furthermore, as discussed in Section 5.1.1, the robot must be able to track the movement of people andobstacles in the environment and consider this movement in its planning. One method of approaching thisproblem is the idea of Reflective Navigation (Kluge, 2004), wherein the robot assumes that things movingin the environment are intelligent agents with similar obstacle avoidance tendencies to its own. In order toaccurately predict the future trajectories of people moving in the robot’s environment, the robot may needto learn common paths that people take (e.g. Bruce and Gordon, 2004; Bennewitz et al., 2005; Foka, 2005).However, we currently anticipate that such behavior will not be necessary to achieve socially acceptablerobot behavior.

For this portion of the thesis, we will investigate the simple task of having a robot navigate through apopulated hallway. As such, the task definition is not particularly complex, consisting primarily of a goaldestination combined with obstacle avoidance. Rather, this portion of the thesis focuses more on the socialconventions and combining the constraints; a more complex task is addressed in Section 5.2.

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(a) (b)

Figure 8: Two possible paths for an agent to take around an obstacle. Without additional knowledge,path (a) appears to be more efficient (shorter).

Figure 9: The approach situation of Figure 7, but shown in global context. If the agent’s goal locationis at the star, then the agent’s most efficient path around the obstacle is to pass it on the right side.

Social conventions. As we identified in related work and in our retirement community observations,people use a wide variety of societally-defined conventions when walking around each other. Some of theseconventions may be physically impossible for a robot to observe; even a simple greeting of a smile and nodrequires an expressive and mobile head, something not all robots possess. Other conventions may requireadvanced sensing capabilities, such as reacting differently according to a person’s gender or social status,which a robot may have difficulty determining.

From the literature discussed in Section 2.1, we currently anticipate that the two most important so-cial conventions that a robot must follow are tending to the right side of a hallway and avoiding people’spersonal space zones. Both of these conventions can be represented as navigational constraints in a straight-forward manner, as discussed in our General Approach. Whether additional conventions are necessary willbe determined during our design and evaluation of the navigational algorithm.

Combining constraints. As discussed, we can define a series of constraints based on the task definition,societal conventions, and the robot’s kinematics and dynamics. In addition, we must define some measure ofefficiency, likely based on travel distance and energy requirements to change speed and direction, which therobot must attempt to optimize. Once each of these are defined, we must determine how to combine them tocompute the robot’s velocity at any given time. However, the constraints defining the robot’s behavior mayat times conflict with one another. For example, the most efficient path through a crowd may not respect the

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constraint against entering the personal space of others. On the other hand, just as it is socially acceptableto nudge other people in particularly dense crowds, the personal space constraint may need to be relaxed insome circumstances or the robot may fail at its task. Thus, the different constraints need to be combined insome way to resolve such conflicts.

One method of combining the constraints would be to treat the system as a linear programming modeland assign individual weights to each constraint in the optimization function, which could then be solved byany number of standard constraint solvers (Williams, 1999). However, due to the need for global planningto determine the most efficient path, it is not clear at this time that a mathematical model will be the moststraightforward method. Furthermore, we do not have evidence at this time that the system will be strictlylinear. We may also consider using Multipartite RRTs for planning in high-dimensional constraint space(Zucker et al., 2007) combined with heuristic biasing to probabilistically optimize efficiency (Urmson andSimmons, 2003). We are continuing to research additional methods of combining the different constraints.

Within each task, determining the relative weights (which may or may not be linear) to assign to eachconstraint can be seen as a learning problem. In particular, we wish to learn how to combine the constraintsto produce the most human-like behavior. Thus, will compare the robot’s behavior under various constraintcombinations against actual human behavior, as discussed below.

5.1.3 Evaluation

Our primary method of evaluating the robot’s navigational ability will be through quantitative user studies.Because this portion of the thesis focuses on the robot’s ability to navigate around people in the environment,we anticipate that the studies will require having multiple people walking around an area where the robot ismoving, and comparing the robot’s behavior to human behavior as well as using questionnaires to determinepeople’s perceptions of the robot’s movements. In particular, we will need to study the robot’s behaviorin varying levels of crowdedness and clutter to verify that its behavior remains socially acceptable. Thesestudies will be used to understand people’s overall responses to the robot and to determine appropriateweights for the different navigational constraints.

Example procedure. We will recruit people in groups of 3-6 people. Each group will be asked to performsome task that requires them to move around an environment, such as moving small items from one end ofa hallway to another. At some point during the task, the robot will travel through the environment usingone of the following navigational methods: our TCE-based method (with one of several sets of constraintcombinations), a standard obstacle avoidance algorithm that does not account for social conventions, anda Wizard-of-Oz method where the robot is controlled by a human operator. We will also obtain baselinemeasures of a person walking through the same environment.

Measures. We will collect both objective and subjective measures of the robot’s performance. We willmeasure each algorithm’s functionality in terms of the robot’s ability to navigate without collisions whilerespecting social conventions such as personal space, the time it requires to travel through the environment,and how much people change their own behaviors in response to the robot. We will also collect participants’perceptions of the robot via questionnaires after each trial.

Long-term evaluation. Since people’s reactions to the robot may change over time, we may also attemptlong-term studies of the robot’s behavior. Such studies may require having the robot moving around in abuilding on a daily basis and measuring how people’s behaviors around the robot change over time, such aswhether people attempt to move out of the robot’s way or simply ignore the robot.

5.2 Side-by-side leading

Once we have a navigational algorithm that can obey social conventions, we will extend it to perform side-by-side escorting. In particular, we will add the task constraint of remaining side-by-side as well as extendthe notion of efficiency within the context of a joint task.

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(a) (b)

(c) (d)

Figure 10: In these images, the “social distance” between two people is shown as a dashed lineconnecting them. Note that the social distance is measured around the personal space of others, asin (b) and (c), but is not influenced by inanimate objects, as in (d).

For two partners to walk together, they must maintain a certain lateral distance between them. They mayoccasionally pass on opposite sides of an obstacle (such as a chair or a pillar) as long as their interpersonaldistance is not influenced greatly. If the partners approach a person, however, social convention dictates thatthey should attempt to pass the person on the same side, rather than surrounding the person. We argue thatthis convention can be understood as part of the task definition by claiming that presence of a third personbetween the two partners increases the social distance between them, thus influencing the task-constrainedinterpersonal distance. Our current working definition of this “social distance” is as follows.

Definition 1 (Social Distance) The social distance between two agents is the shortest distance betweenthe two that does not overlap any other agent’s personal space zone.

Examples of the social distance between two people are shown in Figure 10. In general, constraining thesocial distance between a robot and a person will still allow the partners to pass on opposite sides of aninanimate object, since it will not influence the social distance, but will limit the circumstances in whichthey will do so around a person. That is, they will only pass on opposite sides of a person when the socialdistance constraint is outweighed by other constraints, such as the dynamic constraints that may occur if aperson approaches the pair suddenly and unexpectedly.

Additionally, this navigational algorithm must be able to account for the ways in which people maneuveraround obstacles and bottlenecks when walking together (see Section 4.4). When encountering an obstacle,one partner may speed up to move ahead of the other or slow down to move behind, both partners maymove to one side, or both partners may move to opposite sides. We argue that these behaviors are chosenopportunistically; that is, each partner chooses his action based on what he believes to be the most efficientjoint behavior. To implement this in the TCE framework, the robot must thus attempt to optimize jointefficiency between itself and the human. We define “joint efficiency” as follows.

Definition 2 (Joint Efficiency) We define joint efficiency to be a measure of the effort required by bothparties when acting jointly. This includes the energy required to follow a certain path or change direction aswell as the cost of effective communication for maintaining common ground.

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Using these definitions with the description of side-by-side escorting as given in Appendix A, we canformulate the task of escorting as the following set of constraints.

• Task definition

– Minimize deviance from optimal social distance between self and partner.

– Minimize probability of collisions.

– Minimize deviance from desired goal/path.

– Minimize deviance from desired speed.

• Societal conventions

– Bias heading toward the right side of hallways.

– Minimize intrusion into the personal space of others.

• Efficiency

– Minimize the expected joint effort between self and partner.

5.2.1 Implementation

Side-by-side leading is an extension of the robot’s ability to navigate around people. The algorithm discussedpreviously will be extended to consider additional task rules as well as joint efficiency.

5.2.2 Evaluation

Again, evaluation will be primarily based on quantitative user studies. In particular, we will test the robot’sescorting behavior in a tour guide task, in which the robot will lead an individual around a building. Aftera tour, the participant will be asked to rate the robot’s behavior along several scales, including generalpreference, human-likeness, and predictability. In addition, we will compare the robot’s performance againstmore traditional robotic tour-guiding techniques in which the robot leads in front of the person. However,to reduce implementation effort, we may test the traditional leading technique with a Wizard-of-Oz method.

5.3 Side-by-side following

As a stretch goal, we may additionally attempt to implement side-by-side following. Following differs fromleading primarily in that it has fewer task rules; the task rules can be summarized as:

• Minimize deviance from optimal social distance between self and partner.

• Minimize probability of collisions.

However, we anticipate two main difficulties with the side-by-side following task. First, a person-followingrobot must be extremely reactive to the person’s movements, which requires a very accurate sensor model ofthe leading person. Second, we anticipate that understanding the joint effort will be more difficult from thefollower’s perspective than from the leader’s perspective, as it has a much more predictive nature. In mostsituations, the leader can perform an action with the expectation that the follower will correctly interpretthe action and react appropriately. The follower, on the other hand, must accurately detect the leader’sactions and predict its future movements.

5.4 Robotic platforms

We have several robotic platforms available on which to implement and test our work. We have traditionallyused Grace (Simmons et al., 2003) for this work, and will continue development on that platform. In addition,we are currently developing a new, highly mobile base. Either robot will be able to be equipped with multiplelaser rangefinders to provide a 360◦ field of view.

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(a) Grace, an RWI B21 robotthat we have used in varioushuman–robot interaction tasks.

(b) Preliminary design sketches fora new holonomic robot.

Figure 11: The two robots that may be used in this research.

5.4.1 Grace

Grace is an RWI B21 robot that we have used in various human–robot interaction tasks, including standingin line (Nakauchi and Simmons, 2000), tending to the right side of hallways (Olivera and Simmons, 2002),and the AAAI Challenge (Simmons et al., 2003), as well as our recent work on person-following (Gockleyet al., 2007). It is a non-holonomic base in that it can drive only in the direction it is facing; it cannot travelsideways without a parallel-parking maneuver. Grace is shown in Figure 11(a).

5.4.2 Holonomic robot

We are currently working with Botrics1 as well as members of the School of Design to develop a holonomicrobot based on the Obot H100. The robot uses three omni-wheels based on the Killough platform (Pin andKillough, 1994). Unlike Grace, this robot will be able to move sideways without changing its orientation,which will allow it to side-step obstacles without having to turn, for example. We believe that this extramobility will afford more human-like behaviors. In addition, we are designing a body for the robot consideringthe following constraints:

• The body of the robot should have a more organic shape than is typical of most research robots (suchas Grace), which are often likened to trash cans.

• The robot should be tall enough to be noticeable when it is amongst standing people, but it shouldnot feel intimidating to its interaction partners. Preliminary investigations suggest a height of approx-imately 4.5’ to be appropriate for most adults.

• Finally, the robot’s body should not suggest skills beyond its capabilities. In particular, it should notbe overly human-like, it should not have arms if it cannot gesture or hands if it cannot grasp, it shouldnot have ears if it cannot process speech input, and so on.

1http://www.botrics.com/

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Preliminary design sketches are shown in Figure 11(b). However, building this robot is not intended to be amajor contribution of this thesis; it is discussed here only as a potential platform for implementation of thenavigational algorithms we have described.

5.5 Summary

In summary, we are addressing the problem of having a robot navigate with and around people in a sociallyacceptable manner. We are proposing a new method of describing physically interactive social tasks as aseries of task-based, societally-based, and efficiency-based constraints to the robot’s navigational algorithm.We will use this framework to develop a general algorithm that will allow a robot to navigate around peoplein a socially acceptable manner. We will then extend the algorithm to allow the robot to lead a personside-by-side. We will examine the robot’s behaviors using user studies to verify its human-likeness and socialappropriateness as well as its ability to complete tasks. We believe that this framework will allow us togreatly improve the behavior of robots navigating with and around people over traditional robot controlalgorithms.

As a final note, the TCE framework as proposed is largely conceptual in nature. That is, the frameworkis intended as a means of understanding human–robot social tasks and as a basis for implementation, butwe do not currently intend to produce a full implementational framework. We believe that there are arelatively small number of task constraints that could apply to a comparatively large number of socialtasks, thus allowing for a very general, versatile implementation of human–robot interaction within the TCEframework. However, proof of generalizability would first require many more analyses of human behaviorsin order to derive commonalities, which is not a focus of this thesis at this time. We anticipate that atthe conclusion of this thesis we will be able to generalize across the tasks that we have studied, specificallyperson-following, social navigation through hallways, and side-by-side escorting.

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6 Timeline

We anticipate that the proposed research will be completed according to the following timeline.

• Summer and Fall 2007

– Continue to explore social conventions for these tasks.

– Implement and test laser-based dynamic person- and obstacle-tracking.

– Begin design and implemention of basic controller with obstacle avoidance and social conventions.

• Spring 2008

– Complete basic navigational controller.

– Begin user studies for tuning constraints and determining user preferences.

• Summer 2008

– Translate side-by-side social conventions into controller parameters.

– Complete user studies for basic controller.

• Fall 2008

– Implement side-by-side escorting.

– Begin tests for human-likeness and user acceptance of robot escorting.

• Spring 2009

– Complete user testing of escorting.

– Write and defend thesis.

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A Rules for walk-with

The following rules were derived from our study of people walking together (see Section 4.4). Note that thereare fewer rules for following than for leading; this results from the more reactive nature of person-following.Many of the rules listed for leading are general to individual-level navigation.

A.1 Rules for leading

Task definition: determine and follow path to goal while maintaining appropriate side-by-side social distanceon the right side of the person following the robot.

Rule ClassificationFollow pre-determined path taskTend to right side of hallway societalIf the person falls more than 1m behind: assume person can’t keep up,reduce velocity taskIf person falls too far behind or stops unexpectedly: slow down to stop,turn to watch person, wait until person catches up efficiencyIf person has stopped and doesn’t resume in some amount of time, ask why(may need to declare task failure) taskIf person turns away unexpectedly (i.e. their velocity significantly changesdirection without a visible obstacle):

Temporarily follow person to maintain proper distance taskMay be able to replan path accounting for turn efficiency

To turn right (i.e. toward the person): speed up while turning (cut off person) efficiencyTo turn left (i.e. away from the person): slow down while turning efficiency

If person does not react after some distance, stop and offer instructions taskIf an obstacle is heading toward the pair, the robot should plan to pass iton the right. societalIf an obstacle is headed away from them (i.e. robot and person are catchingup to someone), it should plan to pass on the left societal

Side doesn’t matter for stationary obstacles societal + efficiencyWhen obstacle is a person, the robot and partner should pass on the same side task + societalWhen obstacle is not a person, passing side does not matter task + efficiency

If obstacle is in front of person but not robot:If room: robot should move to allow person to remain side-by-side task + efficiencyOtherwise: robot should go straight (maintain or increase speed); can expectperson to fall behind and then catch up. Due to the geometry, the personwill be forced to either fall behind or speed up to pass in front of the robot.If person falls behind but doesn’t try to catch up, treat as above rule fora person who has fallen behind. If the person speeds up, the robot shouldmaintain speed and try to restore side-by-side travel after passing the obstacle efficiency

If obstacle is in front of robot but not person: robot should slow down and allowperson to go first efficiency

If robot is significantly ahead of person, it can speed up to pass in front efficiencyIf person stops, robot may need to speak (e.g. “Please go ahead”) or may goaround person and go first taskNeed to speed up after it’s past the obstacle to resume default position task

If obstacle is in front of both person and robot: determine whether any patharound the obstacle exists, take most efficient task + efficiency

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A.2 Rules for following

Task definition: maintain appropriate side-by-side social distance on the left side of the leader.

Rule ClassificationDefault: attempt to match person’s position (with offset) and velocity task + societalIf no obstacle is visible:

If person begins moving away from robot (e.g. to the right), anticipate a turn:speed up while arcing right efficiencyIf person begins moving closer to the robot (e.g. to the left), anticipate a turn:slow down while arcing left efficiency

If obstacle in front of robot but not person:If obstacle is a person, robot must pass on the same side as its partner task (social distance)If person’s velocity increases or does not change, then slow down and fall behind efficiencyIf person slows down, try to speed up and pass in front efficiency

If obstacle in front of person but not robot:If person moves closer to robot, attempt to move aside as well efficiency + taskIf no space and/or person speeds up, then slow down to allow person to goahead, fall behind and then catch up efficiencyIf person slows down, continue in straight line and expect person to fallbehind and then catch up efficiency

If obstacle in front of both robot and person: wait for person’s reaction, follow task

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