Investigating spatial relationships in human-robot interaction

Transcription

1 Investigating spatial relationships in human-robot interaction HELGE HÜTTENRAUCH KERSTIN SEVERINSON EKLUNDH ANDERS GREEN ELIN A TOPP Human computer interaction (HCI) Computer science and communication (CSC) Royal institute of technology (KTH)

2 HCI-31 In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2006), Oct. 9 15, 2006, Beijing, China {hehu, kse, green, topp}@csc.kth.se Human computer interaction (HCI) Computer science and communication (CSC) Royal institute of technology (KTH) S Stockholm, Sweden URL:

3 Investigating Spatial Relationships in Human-Robot Interaction Helge Huettenrauch, Kerstin Severinson Eklundh, Anders Green, Elin A.Topp School of Computer Science and Communication (CSC) Royal Institute of Technology (KTH) Stockholm, Sweden {hehu, kse, green, Abstract - Co-presence and embodied interaction are two fundamental characteristics of the command and control situation for service robots. This paper presents a study of spatial distances and orientation of a robot with respect to a human user in an experimental setting. Relevant concepts of spatiality from social interaction studies are introduced and related to Human-Robot Interaction (HRI). A Wizard-of-Oz study quantifies the observed spatial distances and spatial formations encountered. However, it is claimed that a simplistic parameterization and measurement of spatial interaction misses the dynamic character and might be counterproductive in the design of socially appropriate robots. Index Terms spatiality in human-robot interaction. I. INTRODUCTION Humans engaged in physical activities deal with spatial relationships. The physical mass and degrees of freedom of body, head, and limbs need to be orchestrated for movements or manipulation based upon sensory perception and cognitive abilities. The necessary understanding of spatiality is claimed to have its origin in evolutionary traits that shaped not only perception, but influenced human usage of linguistic metaphors in daily usage [17]. A service robot that operates in the co-presence of a human user might become engaged in activities that are determined by the human s and the robot s co-presence, mobility, multimodal communication, and embodied interaction [6]. Trained by daily experience humans are in general skilled in dealing with other people in managing space and in handling objects. The signaling, whether through nonverbal or verbal expressions is well understood, building upon the ability to notice other people s body movements, gaze exchanges, gestures or mimic expressions [4]. Furthermore signaling in and through the environment is possible and anchored within the sociocultural context and practice [2], e.g., a closed door can signify a please do not disturb me right now if this convention is well established and adhered to. Interactive mobile robots are machines that test many human assumptions about interactive artefacts by pushing the borderline of our understanding, differentiation, and Fig. 1 User teaching the robot objects reactions towards what is alive or inanimate [16], [20]. The robots self-locomotion and the attribution of body - movement as expression of own intentions are contributing factors. As humans and robots interact, this attribution of character towards robots might influence humans spatial behaviour in the presence of such devices. This paper investigates the spatial management in a Human-Robot-Interaction scenario as illustrated in figure 1: A user guides her new robot around with a follow-me behaviour and shows it the operation area. In this way the user is teaching the robot places and objects that will allow the robot to perform service missions afterwards. At the intended locations, the user and the robot need to position themselves so that objects or locations can be shown and named to the robot by speech dialogue. Our research questions related to the scenario can be phrased like this: How do the spatial distances and orientations of a user in relation to a robot vary throughout a cooperative task performed in a home-like environment? Can patterns of spatial HRI behaviours be identified to guide the design of robots spatial conduct? To investigate these questions we performed a study with 22 subjects where we observed and recorded the movement and positioning during this interaction to X/06/$ IEEE

4 understand how robot motion and interaction behaviours can be designed to be perceived as socially appropriate. We are interested in this spatial management behaviour as it requires the active monitoring and dynamic reaction to each others movement and position changes. We also want to determine how the robot should select appropriate movement behaviours when interacting with a human user in a spatial context. Understanding posture and positioning changes in HRI are prerequisites to reading one another s signalling through joint spatial management. It is assumed that it is used in parallel to other communication modalities like spoken utterances. To find the relevant features of such spatial interaction between a robot and a user we let a robot interact with users and analysed the interaction for variations in distance and spatial orientation. The remaining paper is organized as follows: The background to relevant concepts from social interaction studies and related research in robotics is given in the next part. In Section III the user study conducted is presented. Finally, in section IV we discuss the findings of the study. II. BACKGROUND Many disciplines contribute to our understanding of spatial (inter-) action in co-presence of people and (interactive) artefacts. Below relevant concepts such as Hall s Proxemics and Kendon s F-formation system are introduced and discussed for their possible significance in HRI. A. Hall s Proxemics Hall studied interpersonal distances and coined the term Proxemics [10], i.e., the interrelated observations and theories of man s use of space as a specialized elaboration of culture [ibid, p.1]. In the human-robot-interaction context of posture and positioning, mainly three findings are of importance: The classification of interpersonal distances into 4 different classes, the realization of cultural differences in the spatial behaviour, and last but not least man s perception of space. From his observations in the USA, Hall concluded that social interaction is based upon and governed by four interpersonal distances: intimate ( meter), personal ( meters), social ( meters), and public (>3.66 meters). The combination of measurable spatial parameters, human ergonomic and kinetic capabilities, different social roles and interaction as well as typical characteristics and interaction situations make Hall s interpersonal distances interesting for HRI. It might be hypothesized that the most co-present HRI exchanges and reciprocal adaptations between a human and a robot will happen in the social and the personal distances. The public distance is of interest as this seems like an appropriate distance to perhaps try to signal that an exchange can or is about to happen. The social and the personal distance seem appropriate in theory to facilitate both the communication and the exchange of goods (for example the manipulation with a robotic arm). The intimate distance seems to be better suited for exchanges with, e.g., so called mental commit robots like the seal-robot Paro [18], where touch is an intended interaction modality. B. Kendon s F-formation system Kendon s F-Formation system [12] is based upon the observations that people often group themselves in a spatial formation, e.g., in clusters, lines, circles, or other patterns. The term formation is used to express the dynamic aspect of this spatial arrangement, i.e., the need to actively sustain it during interaction. This can be observed as small, well synchronized movements of the participating interactors. An F-Formation arises when two or more people form a shared space between them to which they have equal and direct access due to their sustained spatial and orientational configuration. The necessary behavioural organization and movement patterns which are used to sustain this F-Formation is called an F-Formation system. The F-Formation system can be applied directly to an interactive encounter between a robot and a human: Between the two a so called transactional segment or o-space is established (marked with ellipses in figure 2), i.e., a space that both participants are able to look and speak into, and in which they can handle objects of shared interest. Fig. 2: Kendon s F-formation arrangements Kendon showed that joint activities and spatial interactions are supported by certain F-Formation system arrangements, and thus often are encountered in prototypical situations. In the Vis-à-vis arrangement (figure 2, left) two participants normally face one another directly; an L- Shape arrangement (see fig. 2, middle) usually indicates a joint system in which something is shared in the o-space. The Side-by-side configuration (fig.2, right) allows two participants to stand closely together and to face the same direction. This arrangement often occurs in situations were both interactors are facing an outer edge given externally by the environment, e.g., in the form of a table or a wall. For HRI it is important to notice that all F-formation arrangements support a triadic relationship between the two interactors and one or more objects of shared interest, e.g., objects that a robot should learn. C. Spatiality in HRI Several systems have been designed or studied to enable the robots to actively manage spatiality in interaction with humans. Yoda and Shiota [22] take the need for safety in passing a human in a hallway as motivation to develop control

5 strategies for the robot. Three types of encounters were anticipated as test cases for their control algorithm, including a standing, a walking, and a running person. Nakauchi and Simmons [13] present another approach by first collecting empirical data on how people stand in line. They use these data to model a set of behaviours for a robot that needs to get into a queue, wait and advance in the queue for being serviced along with other people there. Butler and Agah [3] varied a robot s movement behaviours and performed a user study to evaluate how different robot speeds and distances were perceived by users. However, no interactive task was performed by the robot or user during this experiment. A study reported by Althaus et al. [1] used a complex, room-based sensor array to track the fine movements and spatial adaptations of a group of people and a robot during its initial appearance, its joining of the group, and finally, the robot s departure. The authors concluded that the spatial adaptation observed for the humans could be matched by the robot s reacting (in turn) with a dynamic adaptation in its positioning. Prassler et al. [15] introduced a robot wheelchair control system that allowed the system to stay close to an accompanying person in a crowed subway station, i.e., the robot movement (with a person) in a highly dynamic context could be demonstrated. Other people (besides the accompanying person) in this public space were treated as dynamic obstacles that needed to be avoided. In [19] Topp and Christensen also addressed the dynamic, joint movement of a robot and its user. However, their robot operation setting is confined to an indoor office space. The interaction is focused on providing a robot navigation component that can follow users with its laserbased tracking system during a so called Human Augmented Mapping mission. Using Hall s interpersonal distances as parameters in a robotic system, Pacchierotti et al. [14] recently devised an algorithm that allows robots to pass people in hallways. III. USER STUDY To investigate the spatial distances and orientations of a user interacting with a robot we designed a study based upon the idea of a Home Tour [5], where a user shows a robot around and teaches it places and objects in a office or home-like environment. A. Scenario and setup In our trial scenario a user has received a robot and is ready to use it for the first time. To introduce the robot to the environment it needs to be shown around to learn relevant places and objects. Once the robot has learned these the user is encouraged to test the robot. Users could send Fig. 3 Living-room experiment area the robot on a search- or a find mission to verify that it could find locations or previously encountered objects. The task embedded in the HRI scenario was thus for invited trial users to (a) get familiar with the robot and navigate it by letting it follow him or her, (b) teach it places and objects, (c) validate already taught places and objects, and (d) handle interaction practically with the robot, including an initial opening and a closing. The robot used in this study is an ActivMedia Performance PeopleBot 1. It comes equipped with an on-board pantilt-zoom camera. Trial users were told that this camera was employed by the robot for object and place recognition. They were also informed that the microphones placed upon the robot were used by the interactive speech system enabling the commanding of the robot by speech. The trial was conducted in a room approximately five by five meter in size. It is furnished with IKEA living room furniture, including different tables, a bookshelf, and two sofas (see figure 3). Indicated with numbers are the entrance, the bookshelf, the Wizard of Oz control station (with a video camera), a small table with a telephone, a low coffee table upon which different objects like a remote control and magazines were placed, two sofas, a TV and a VCR combination placed on a small table, and finally, a small dining table with a fruit bowl, a coffee cup etc. The trial subjects were recruited within the Royal Institute of Technology, i.e., young technical students of both genders. Requirements for selection were that they did not work with or performed research in robotics or computer vision, as this was judged to be the requirement of a robot 1

6 encounter with inexperienced users. We conducted 22 trials (after 4 initial pilot trials for trial-adjustments) with 9 women and 13 men. Participants of the study were rewarded a cinema ticket for their time and effort. Upon arrival participants received an introduction to the robot and the task, both in written form and in the form of a short demonstration by one of the experiment leaders. They were then asked to use the robot to teach it new places and objects and validate these. A time-limit was set for the interaction, i.e., after 15 minutes a sound indicating empty batteries for the robot was played to end the trial. Upon completion of the experiment users were asked to fill in a questionnaire before being debriefed and told that the robot s behaviours during the experiment were simulated. The robot behaviours were controlled by two experiment leaders who used a wireless robot navigation and onboard-camera control and a speech synthesizer to produce spoken dialogue in a Wizard-of-Oz setup [8]. B. Data collection Multiple sources were used for data collection during the trial: An external video camera taped the trial in audio and video from the experiment leaders position and perspective. Placed in the room s corners, four webcams running at a frame-rate of about 1 Hz recorded the interaction. The images taken with the webcams ensured that the user and robot movements, postures, and gestures would be captured from different angles to avoid possible occlusions. Data from a laser range finder on the robot were stored and analysed with the help of a person tracking system [19]. This data represents information about the spatial distance and positioning of the user under the condition that the user is in a 180 degree half-circle in front of the robot. A system log stored all commands that were sent to the robot. The different systems mentioned were synchronized against a local Network Time Protocol (NTP) server. Together with the timing information the robot trials can thus be run in a simulator at a later point of time. Finally, a digital recorder was used to record the spoken commands on the robot itself for detailed speech dialog analysis and future speech recognition training. C. Data analysis To find the relevant spatial interaction patterns and ways to categorize them, we first carefully examined the data of a few trials. After this first round of finetuning we settled for our analysis on a process as follows. As starting point for the analysis the timeline of the external video was taken to synchronize the interaction transcriptions. Based upon these synchronized transcriptions the interaction was then categorized into three interaction episodes termed FOLLOW (user guiding the robot around), SHOW (user teaching the robot places and objects), and VALIDATE (user testing the taught Figure 4: Visual inspection tool places and objects by sending the robot on missions to find them again). Another category of interaction was termed BREAKDOWN, i.e., scenes where miscommunication and /or task-level incidents led to interruptions in interaction. Often this was accompanied by repair attempts through speech dialogue, adaptations of position towards the interaction partner, speech-command repetitions, or a change of interaction strategy altogether (see [7] for details). For each of the identified interaction episodes the initial posture and positioning, i.e., the distance, orientation, body posture, gesture(s), utterances, and dynamic positioning changes within the episode itself were annotated. The spatial formation of the user and the robot was analysed with help of the laser range finder data and a visual inspection tool (see figure 4). As the laser range finder data is only available when the user is standing in a 180º degree half-circle in front of the robot, the visual inspection tool was applied in situations in which the user was standing behind the robot or laser data was unavailable. The visual inspection tool displays different webcam images simultaneously and supports the annotation of the posture and positioning by pressing pre-defined keys on a keyboard. Before loaded into this visual annotation tool, still images are first overlaid and fused with a calibration image so that virtual dots on the images mark a grid to calculate distances and positions with. With this aid, marks in the trial environment that could possibly bias users to align themselves with were avoided. Image sequences can be played back and forth and give the possibility to quickly annotate movements, positioning, and postures. The outcome of the analysis has been termed a thick description giving the literally frame-by-frame commented observations from the trials. These thick descriptions are accompanied with numerical, quantitative interaction episode descriptions (including still image-sequences for illustration) for each of the observed Follow-, Show-, and Validate episodes. This analysis has been conducted for 11 trials so far, i.e., only half of the available data have been subjected to this in-depth spatial interaction analysis. Focusing on the questions posed initially with respect to the spatial distance and formations of the robot and the human, the following section will focus on the results of

7 the spatial management during the Follow, Show, and Validate episodes as analysed from eleven trial sessions based upon a total of N=321 HRI initiations. A. Findings Tables 1-3 give the summarized findings for the HRI episodes Follow, Show, and Validate as introduced above for eleven trial subjects. Column 1 (numbering from left to right) gives the trial-subject s identity, column 2 holds the number of episodes encountered. Episodes themselves were then categorized according to Hall s interpersonal distances of Intimate, Personal, and Social depicted in columns 3-5 by checking the metric distance between the robot and the user and classifying it according to the appropriate Hall distance. TABLE I FOLLOW-EPISODES ANALYSIS TABLE II SHOW-EPISODES ANALYSIS TABLE III VALIDATE-EPISODES ANALYSIS Finally a categorization according to Kendon s F- formation arrangements was made: Columns 6-8 give the number of events recorded as Vis-à-vis, L-Shape, or Side-by-side F-formations. Note that the subtotals do not necessarily have to add up to the absolute number of episodes. The reason is that subjects also initiated missions while not being in one of the Kendon F-formations analyzed. Subjects were free to decide for themselves how to conduct the trial in detail. Some choose to first iterate FOLLOW and SHOW missions to teach places and objects to the robot before trying VALIDATE-missions with a few selected places and objects. As an alternative strategy subjects could keep a strict sequential order of FOLLOW, SHOW, and VALIDATE after one another. The preference to iterate multiple Follow- and Show-missions first as well as the observation that Validate missions are taking longer in duration than Follow- or Show-missions explain why only 93 Validate missions were observed. For the Hall s interpersonal distances it is striking how predominant the Personal zone is, i.e., independent upon mission-type subjects preferred to position themselves in the range of 1 to 4 feet (0.45 to 1.2 meters). Interesting is that the number of subjects who command a Follow-, Show-, or Validate-mission from the intimate zone is much smaller than, for example the robot approach distance reported in Walters et al. [21]. The authors requested subjects to move toward the robot as far as they felt comfortable and reported that up to 40% of their subjects came closer than 0.45 meters. Our figures on users entering into an intimate distance towards the robot are much smaller as given in Table 1-3 above, e.g., for Follow = 5 (4.6%); Show = 7 (5.8%), and Validate = 12 ( 12.9%) of users ordered the robot to perform a mission while being in the intimate Hall distance. Although both experiments used an ActivMedia PeopleBot 2 the high number of people coming very close to the robot was not encountered in our experiment. Looking at the Kendon F-formations a dominance of the Vis-à-vis (or face-to-face) positioning of the user towards the robot can be noted, independent upon interaction episode. The L-Shape F-formation arrangement is in comparison less often observed. Especially in the Followepisodes the L-Shape formations are rarely encountered. The Validate and the Show episodes seem more appropriate to be handled in an L-Shape formation as can be seen from the more frequent occurrences. Especially for the Show episodes, used to present and label objects and places in the environment for the robot, the formation of the L-Shape seems to be more natural. 2 Note that Walters et al [21] had modified their robot: The on-board camera position was different additionally, a lifting arm was put on the robot.

8 Fig. 5 Robot centric laser data plot showing distance between robot and subject during different interaction episodes Side-by-side F-formation arrangements were rarely encountered; most often they occurred in the Follow episode. This spatial formation facing an outer edge together is likely very dependent upon the environment in which the human-robot interaction is conducted. The setup in the living room, e.g., in furniture, might, beside the bookshelf, simply not provide the situation of this formation to appear very often. An important limitation to tables 1-3 above should be explicitly mentioned: Each occurrence in the table is based upon a clearly identifiable, often speech-dialog initiated, interaction episode of Follow, Show, or Validate. It is thus the starting point that was taken as marker of the spatial relationship between the robot and a subject. This limits the categorization to a static perspective, i.e., the dynamics of change over (even short) time periods is not covered. The fact that this missing dynamic aspect might however have deeper implications can be seen in figure 5. It shows the laser tracking plot of a subject s distance 3 from the robot centric perspective. The user is approaching the robot (coming into the view of the laser range finder) and starts the Follow-1 episode (depicted through boxes below the graph) after a short while standing still in front of the robot at a distance of about 1.2 meters. After spoken dialogue initiation the subject takes a step from the robot and waits for the robot s initial movement as feedback (visible as an increase of the distance, then again a decrease). When the robot starts its movement this feedback signal is taken up by the subject who starts going 3 as a graphical reduction, orientation data of the subject was removed; towards a corner of the room, rapidly increasing the distance towards the robot (peaking at about 2.3 meters). Arriving at a goal-position the subject stops and turns around waiting for the robot. The robot s approach towards the non-moving subject gives a sharp falling flank at the end of Follow-1. Once the robot has reached the subject s position the trial participant makes an observable position and orientation switch that mark the beginning of the following two Show -episodes. These are then initiated after one another without noticeable changes in position from the subject. This is shown through the almost horizontal (distance-) line of Show-1 and Show-2. Note however the small position changes in distance just before and at the end of the Show -episodes (pointed out by arrows in the graph). Almost none-noticeable in the video-data, these small alignment movements can be found in the data to often signify transitions from one interaction episode into another. We find these micro-spatial adaptations interesting as they might in the future provide a possibility to try sensor-perception-based triggers indicating that new interaction tasks or episodes are prepared for. The subject s mission depicted in figure 5 is continued with multiple Follow -episodes; the illustration example finally ends with another Show. While somewhat disturbed by laser-sensor jitter, even the Show-4 episode is characterized by a straight horizontal line. From the data we have analyzed so far we saw that different HRI-interaction episodes will also produce

9 different spatial patterns in the sensor readings that monitor the (subject) user s movements and positioning. Summarizing, we describe the observed dimensions and differences of the interaction episodes of Follow, Show, and Validate by their characteristics: Follow is best typified by a paired-dynamic and user-initiative driven joint activity which, e.g., can be seen from the dynamics of distance/orientation measurements and high spatial-change frequencies. Show instead has a paired-static, joint interactivity attribution. Movements are confined to small adaptive and co-operative engagements and each of the interactors can be acting or reacting in shaping the interaction progress. Finally and as in our scenario tried, Validate is neither paired, nor tightly coupled. Once initiated from the user the robot is acting autonomously while the user becomes a supervisor monitoring the progress at best, or possibly, starting a side activity altogether. What becomes more important with this type of interaction episode is thus, how both the robot and the user come together again and continue with their joint track of interaction once the Validate-mission has been finished. IV. DISCUSSION A descriptive analysis of static measurements showed that Hall s personal distance, i.e., a distance between robot and user in the range of 0.46 to 1.22 meters was preferred in 73% of the observed Follow-, in 85% of the observed Show- and in 78% of the observed Validate-mission initiations. Furthermore, Kendon s Vis-à-vis F-formation arrangement was found to be prevailing among the spatial configurations tested for. A note of caution was raised to the applicability of the terms of both Hall and Kendon however: The dynamic changes and transitions from one interaction episode state into another one are difficult to express in terms of Hall s interpersonal distances and Kendon s F-formations arrangements. Kendon s F- formations arrangements are dynamically sustained by small position changes, but the lead-in and lead-out into these formations, e.g., from a human and a robot need to be carefully studied. A simplistic parameterization of the preferred Hall distances and Kendon F-formation configurations alone therefore seem unsuited to achieve a socially appropriate robot behavior. A more successful alternative might reside in the attempt to make other robot interaction components aware both of the communicative as well as the coordinating requirements of spatial interaction. Examples would be to allow the spoken dialogue model to trigger spatial behavior-signaling or preemptive robot movements as spatial prompts in HRI [9]. A. Design Implications Findings from this trial might be applied to test the following robot design enhancements and behavior strategies to improve spatial management in HRI: Testing an interactive robot in its targeted usage scenario in an early design phase will reveal spatial management challenges that can be used to improve the robot s performance and HRI; detailed findings might differ according to the context studied. Established user preferences in interaction distance (personal to social) and formation (Vis-à-vis or L- Shape), are dependent upon specific interactionepisodes. The transitions between different interaction episodes should be carefully evaluated and designed for with appropriate HRI spatial management behaviors of the robot. Looking at the available internal-states and perceptual data from the robot s sensory system, e.g., the laserrange finder (figure 5), it seems possible to evaluate the current interaction state and spatial management in HRI by looking for reoccurring and characterizing patterns such as user-quickly-leaving, robotapproaching-standing-user, user-standing-still-andshowing-object, small alignment movements, etc. This context-interpretation could be used as input in the design of an interaction planner. The robot s perceptual capabilities should allow for an extended view on the operation environment and human movements within it; the used 180 degrees in front of the robot (based upon the capacity of the laser range finder) appear too limiting in this regard. A perceptual extension could enable the tracking of user positions in a 360º degree around the robot. This would, for example, give the robot the chance to detect users approaching the robot from behind. B. Future Work The study reported was constrained in several aspects to keep complexity to a level that allowed us to experiment and investigate aspects of the spatial interaction with a robot. Potential directions to extend our work include gradually phasing out the Wizard-of-Oz control elements with working robot system components as a first step. Especially the teleoperation control of the robot s locomotion (and orientation) will be substituted to validate our findings and to compare them with experimental HRI data on spatial positioning that is only governed by implemented robot behaviors. We are also interested in extending our trial setup to multiple rooms, possibly making it necessary to traverse narrow passages together to examine further how elements of the physical environment shape the spatial cooperation between a human and a robot. V. ACKNOWLEDGEMENT The work described in this paper was conducted within the EU Integrated Project COGNIRON ( The Cognitive Robot Companion, and was funded by

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