Robots that can leverage help from people could accomplish much more than robots that cannot. We present the results of two experiments that examine how robots can more effectively request help from people. Study 1 is a video prototype experiment (N=354), investigating the effectiveness of four linguistic politeness strategies as well as the effects of social status (equal, low), size of request (large, small), and robot familiarity (high, low) on people's willingness to help a robot. The results of this study largely support Politeness Theory and the Computers as Social Actors paradigm. Study 2 is a physical human-robot interaction experiment (N=48), examining the impact of source orientation (autonomous, single operator, multiple operators) on people's behavioral willingness to help the robot. People were nearly 50% faster to help the robot if they perceived it to be autonomous rather than being teleoperated. Implications for research design, theory, and methods are discussed.

This paper introduces and empirically evaluates two scaling functions to alter a robot's physical movements based on proximity to a human. Previous research has focused on individual aspects of proxemics, like the appropriate distance to maintain from a human, but has not explored autonomous methods to adapt robot behavior as proximity changes. This paper proposes that robots in a social role should modify their behavior using a continuous function mapped to proximity. The method developed calculates a gain value from proximity readings, which is used to shape the execution of active behaviors on the robot. In order to identify the effects of different mappings from proximity to gain value, two different scaling functions were implemented on an affective search and rescue robot. The findings from a 72 participant study, in a high-fidelity mock disaster site, are examined with attention given to a new measure to determine proxemic awareness. The results indicated that for attributes of intelligence, likability, proxemic awareness, and submissiveness, a logarithmic-based scaling function is preferred over a linear-based scaling function, and over no scaling function. In areas of participant comfort and participant stress, the results indicated both logarithmic and linear scaling functions were preferred to no scaling.

This study demonstrates that robots can achieve socially acceptable interactions using loosely synchronized head gaze-speech acts. Prior approaches use tightly synchronized head gaze-speech, which requires significant human effort and time to manually annotate synchronization events in advance, restricts interactive dialog, or requires that the operator acts as a puppeteer. This paper describes how autonomous synchronization of head gaze can be achieved by exploiting affordances in the sentence structure and time delays. A 93-participant user study was conducted in a simulated disaster site. The rescue robot “Survivor Buddy” generated head gaze for a victim management scenario using a 911 dialog. The study used pre- and postinteraction questionnaires to compare the social acceptance level of loosely synchronized head gaze-speech against tightly synchronized head gaze-speech (manual annotation) and no head gaze-speech conditions. The results indicated that for attributes of Self-Assessment Manikin, i.e., Arousal, Robot Likeability, Human-Like Behavior, Understanding Robot Behavior, Gaze-Speech Synchronization, Looking at Objects at Appropriate Times, and Natural Movement, the loosely synchronized head gaze-speech is similar to tightly synchronized head gaze-speech and preferred to the no head gaze-speech case. This study contributes to a fundamental understanding of the role of social head gaze in social acceptance for human-machine interaction, how social gaze can be produced, and promotes practical implementation in social robots.

We have created a preliminary inference engine for generating gaze acts based on extracting the social context from conversational structure and timing in human-robot dialog.

Based on a synthesis of eight major studies using six robots involving social gaze in robotics, this research proposes a novel behavioral definition as a mapping G = E(C) from the perception of a social context C to a set of head, eye, and body patterns called gaze acts G that expresses the engagement E. This definition places social gaze within the behavior-based programming framework for robots and agents, providing a guide for principled future implementations. The research also identifies five social contexts, or functions, of social gaze (Establishing agency, Communicating social attention, Regulating the interaction process, Manifesting interaction content and Projecting mental state) along with six discrete gaze acts for social gaze functions (Fixation, Short glance, Aversion, Concurrence, Confusion, and Scan) that have been employed by various robots or in simulation for these contexts. The research contributes to a computational understanding of social gaze that bridges psychological, cognitive, and robotics communities.

This paper describes an open source speech translator toolkit created as part of the "Survivor Buddy" project which allows written or spoken word from multiple independent controllers to be translated into either a single synthetic voice, synthetic voices for each controller, or unchanged natural voice of each controller. The human controllers can work over the internet or be physically co-located with the Survivor Buddy. The toolkit is expected to be of use for exploring voice in general human-robot interaction.

Designing and constructing affective robots on schedule and within costs is especially challenging because of the qualitative, artistic nature of affective expressions. Detailed affective design principles do not exist, forcing an iterative design process. This paper describes a three step design process created for the Survivor Buddy project that engages artists in the design process and allows animation to guide physical implementation. The process combines creative design of believable agents unconstrained by costs with traditional design decision matrices. The paper provides a case study comparing the resulting design of the Survivor Buddy 2.0 robot with the original (Survivor Buddy 1.0). The multi-disciplinary methodology produced a more pleasing and expressive robot that was 50% less expensive, 78% lighter, and up to 700% faster within the same amount of design time. This methodology is expected to contribute to reducing risk in designing cost effective affective robots and robots in general.

This paper describes the Survivor Buddy human-robot interaction project and how it was used by four middle-school girls to illustrate the scientific process for an episode of "SciGirls", a Public Broadcast System science reality show. Survivor Buddy is a four degree of freedom robot head, with the face being a MIMO 740 multi-media touch screen monitor. It is being used to explore consistency and trust in the use of robots as social mediums, where robots serve as intermediaries between dependents (e.g., trapped survivors) and the outside world (doctors, rescuers, family members). While the SciGirl experimentation was neither statistically significant nor rigorously controlled, the experience makes three contributions. It introduces the Survivor Buddy project and social medium role, it illustrates that human-robot interaction is an appealing way to make robotics more accessible to the general public, and raises interesting questions about the existence of a minimum set of degrees of freedom for sufficient expressiveness, the relative importance of voice versus non-verbal affect, and the range and intensity of robot motions.