As we introduce social robots into family homes, there is a heightened need to understand both parental and child perceptions of privacy, taking into account family perspectives. Haru4Kids (H4K) is an app-based robot simulator designed to cohabitate with children in their home. In a multi-site investigation, H4K cohabitated with 11 American, 11 Spanish, and 10 Ukrainian families. Interviews preceding and following the cohabitation period with at least one parent and all participating children ages 5–12 allowed us to gauge general expectations and experiences during cohabitation and to estimate their comfort with sharing different kinds of information with the platform. In general, children perceived Haru as a technological device that was social, yet non-human. All stakeholders were more comfortable with general information collection as opposed to information sharing with third parties. Lastly, though culture is known to affect expectations and perceptions of privacy and robots, our study shows cohabitating with H4K led to a general convergence across sites in users’ perceptions of Haru and attitudes towards preserving their privacy with robots.

Conversation can play an essential role in forging bonds between humans and social robots, but participants need to feel like they are being listened to, remembered, and cared about in order to effectively build rapport. In this paper, we propose a novel strategy for conducting small talk with a social robot. Our approach is known as the Tiers of Friendship. It is centered around three core design elements: 1) Persuasive content and character is provided through topic modules created by professional creative writers to ensure engaging conversational content and a compelling personality for the social robot. 2) Conversational memory is achieved by allowing topic modules to specify required information that can be learned through conversation or recalled from previous interactions and organizing topic modules into a hierarchy that enforces information requirements between topics. 3) Dynamicity in conversation is promoted through topic navigation that supports fluid transitions to topics of human interest and employs elements of random ordering to create fresh conversation experiences. In this paper, we show how the Tiers of Friendship can be used to generate conversation content for a social robot that encourages the development of rapport. We describe a working implementation of a small talk system for a social robot based on the Tiers of Friendship that combines off-the-shelf ASR and NLU components and custom robot behavior components implemented via behavior trees on ROS. Finally, in order to evaluate our approach's effectiveness, we conduct an elicitation survey that evaluates conversations in terms of perceived engagement, personality traits, and rapport expectation and discuss the implications for social robotics.

It is easy for human beings to discern whether an observed acoustic signal is a direct speech, reflected speech or noise through simple listening. Relying purely on acoustic cues is enough for human beings to discriminate between the different kinds of sound sources which is not straightforward for machines. A robot equipped with the current robot audition mechanism in most cases, will fail to differentiate a direct speech from the other sound sources because acoustic information alone is insufficient for effective discrimination. Robot audition is an important topic in speech-based human-robot communication. It enables the robot to associate the incoming speech signal to the user for an effective human-robot communication. In challenging environments, this task becomes difficult due to reflections of the direct speech signal and background noise sources. To counter this problem, a robot needs to have a minimum amount of prior information to discriminate the valid speech signal (direct speech) from the contaminants (i.e., speech reflections and background noise sources). Failure to do so would lead to false speech-to-speaker association in robot audition and will gravely impact human-robot communication experience. In this paper we propose to using visual cues to augment the traditional robot audition which relies solely on acoustic information. The proposed method significantly improves accuracy of speech-to-speaker association and machine understanding performance in real environment situation. Experimental results show that our expanded system is robust in discriminating direct speech from speech reflections and background noise sources.

Users of optical see-through head-mounted displays (OHMD) perceive color as a blend of the display color and the background. Color-blending is a major usability challenge as it leads to loss of color encodings and poor text legibility. Color correction aims at mitigating color blending by producing an alternative color which, when blended with the background, more closely approximates the color originally intended. In this paper we present an end-to-end approach to the color blending problem addressing the distortions introduced by the transparent material of the display efficiently and in real time. We also present a user evaluation of correction efficiency. Finally, we present a graphics library called SmartColor showcasing the use of color correction for different types of display content. SmartColor uses color correction to provide three management strategies: correction, contrast, and show-up-on-contrast. Correction determines the alternate color which best preserves the original color. Contrast determines the color which best supports text legibility while preserving as much of the original hue. Show-up-on-contrast makes a component visible when a related component does not have enough contrast to be legible. We describe SmartColor's architecture and illustrate the color strategies for various types of display content.

Users of optical see-through head-mounted displays (OHMD) perceive color as a blend of the display color and the background. Color-blending is a major usability challenge as it leads to loss of color encodings and poor text legibility. Color correction aims at mitigating color blending by producing an alternative color which, when blended with the background, more closely approaches the color originally intended. To date, approaches to color correction do not yield optimal results or do not work in real-time. This paper makes two contributions. First, we present QuickCorrection, a realtime color correction algorithm based on display profiles. We describe the algorithm, measure its accuracy and analyze two implementations for the OpenGL graphics pipeline. Second, we present SmartColor, a middleware for color management of userinterface components in OHMD. SmartColor uses color correction to provide three management strategies: correction, contrast, and show-up-on-contrast. Correction determines the alternate color which best preserves the original color. Contrast determines the color which best warranties text legibility while preserving as much of the original hue. Show-up-on-contrast makes a component visible when a related component does not have enough contrast to be legible. We describe the SmartColor's architecture and illustrate the color strategies for various types of display content.