Matthew K. X. J. Pan

Meet me where i'm gazing: how shared attention gaze affects human-robot handover timing

In this paper we provide empirical evidence that using humanlike gaze cues during human-robot handovers can improve the timing and perceived quality of the handover event. Handovers serve as the foundation of many human-robot tasks. Fluent, legible handover interactions require appropriate nonverbal cues to signal handover intent, location and timing. Inspired by observations of human-human handovers, we implemented gaze behaviors on a PR2 humanoid robot. The robot handed over water bottles to a total of 102 naïve subjects while varying its gaze behaviour: no gaze, gaze designed to elicit shared attention at the handover location, and the shared attention gaze complemented with a turntaking cue. We compared subject perception of and reaction time to the robot-initiated handovers across the three gaze conditions. Results indicate that subjects reach for the offered object significantly earlier when a robot provides a shared attention gaze cue during a handover. We also observed a statistical trend of subjects preferring handovers with turn-taking gaze cues over the other conditions. Our work demonstrates that gaze can play a key role in improving user experience of human-robot handovers, and help make handovers fast and fluent.Categories and Subject Descriptors I.2.9 [Robotics]: Operator interfaces, Commercial robots and applications; H.1.2 [User/Machine Systems]: Human FactorsGeneral TermsExperimentation, Design, Human Factors, Verification.

Characterization of handover orientations used by humans for efficient robot to human handovers

To enable robots to learn handover orientations from observing natural handovers, we conduct a user study to measure and compare natural handover orientations with giver-centered and receiver-centered handover orientations for twenty common objects. We use a distance minimization approach to compute mean handover orientations. We posit that, computed means of receiver-centered orientations could be used by robot givers to achieve more efficient and socially acceptable handovers. Furthermore, we introduce the notion of affordance axes for comparing handover orientations, and offer a definition for computing them. Observable patterns were found in receiver-centered handover orientations. Comparisons show that depending on the object, natural handover orientations may not be receiver-centered; thus, robots may need to distinguish between good and bad handover orientations when learning from natural handovers.

Now where was I?: physiologically-triggered bookmarking

This work explores a novel interaction paradigm driven by implicit, low-attention user control, accomplished by monitoring a users physiological state. We have designed and prototyped this interaction for a first use case of bookmarking an audio stream, to holistically explore the implicit interaction concept. Here, a users galvanic skin conductance (GSR) is monitored for orienting responses (ORs) to external interruptions; our prototype automatically bookmarks the media such that the user can attend to the interruption, then resume listening from the point he/she is interrupted. To test this approachs viability, we addressed questions such as: does GSR exhibit a detectable response to interruptions, and how should the interaction utilize this information? In evaluating this system in a controlled environment, we found an OR detection accuracy of 84%; users provided subjective feedback on its accuracy and utility.

Exploring the Role of Haptic Feedback in Enabling Implicit HCI-Based Bookmarking

We examine how haptic feedback could enable an implicit human-computer interaction, in the context of an audio stream listening use case where a device monitors a users electrodermal activity for orienting responses to external interruptions. When such a response is detected, our previously developed system automatically places a bookmark in the audio stream for later resumption of listening. Here, we investigate two uses of haptic feedback to support this implicit interaction and mitigate effects of noisy (false-positive) bookmarking: (a) low-attention notification when a bookmark is placed, and (b) focused-attention display of bookmarks during resumptive navigation. Results show that haptic notification of bookmark placement, when paired with visual display of bookmark location, significant improves navigation time. Solely visual or haptic display of bookmarks elicited equivalent navigation time; however, only the inclusion of haptic display significantly increased accuracy. Participants preferred haptic notification over no notification at interruption time, and combined haptic and visual display of bookmarks to support navigation to their interrupted location at resumption time. Our contributions include an approach to handling noisy data in implicit HCI, an implementation of haptic notifications that signal implicit system behavior, and discussion of user mental models that may be active in this context.

Galvanic skin response-derived bookmarking of an audio stream

We demonstrate a novel interaction paradigm driven by implicit, low-attention user control, accomplished by monitoring a users physiological state. We have designed and prototyped this interaction for a first use case of bookmarking an audio stream, to holistically explore the implicit interaction concept. A listeners galvanic skin conductance (GSR) is monitored for orienting responses (ORs) to external interruptions; our research prototype then automatically bookmarks the media such that the user can attend to the interruption, then resume listening from the point heshe is interrupted.

Evaluating Social Perception of Human-to-Robot Handovers Using the Robot Social Attributes Scale (RoSAS)

This work explores social perceptions of robots within the domain of human-to-robot handovers. Using the Robotic Social Attributes Scale (RoSAS), we explore how users socially judge robot receivers as three factors are varied: initial position of the robot arm prior to handover, grasp method employed by the robot when receiving a handover object trading off perceived object safety for time efficiency, and retraction speed of the arm following handover. Our results show that over multiple handover interactions with the robot, users gradually perceive the robot receiver as being less discomforting and having more emotional warmth. Additionally, we have found that by varying grasp method and retraction speed, users may hold significantly different judgments of robot competence and discomfort. With these results, we recognize empirically that users are able to develop social perceptions of robots which can change through modification of robot receiving behaviour and through repeated interaction with the robot. More widely, this work suggests that measurement of user social perceptions should play a larger role in the design and evaluation of human-robot interactions and that the RoSAS can serve as a standardized tool in this regard.

Automated detection of handovers using kinematic features

This paper investigates the use of kinematic motions recognized by a support vector machine (SVM) for the automatic detection of object handovers from the perspective of an object receiver. The classifier uses the giver’s kinematic behaviors (e.g. joint angles, distances of joints from each other and with respect to the receiver) to determine a giver’s intent to hand over an object. We used a bagged random forest to determine how informative features were in predicting the occurrence of handovers, and to assist in selecting a core set of features to be used by the classifier. Altogether, 22 kinematic features were chosen for developing handover detection models and later testing of generalization performance. Test results indicated an overall maximum accuracy of 97.5% by the SVM in its capacity to distinguish between handover and non-handover motions. The classification ability of the SVM was found to be unaffected across four kernel functions (linear, quadratic, cubic and radial basis). These results demonstrate considerable potential for detection of handovers and other gestures for human–robot interaction using kinematic features.