Telepresence robots act as the remote embodiments of human operators, enabling people to stay connected to friends, family, and coworkers over lengthy physical separations. However, the factors affecting how humans can best make use of such systems are not yet well understood. This paper explores the effects of personalization and relationship closeness on telepresence via two studies. Study 1 was a between-participants experiment that investigated telepresence robot personalization. 32 pairs of friends (N = 64) participated in the study's team-building-style activities and answered questions about robot operator presence. The results unexpectedly indicated that relationship closeness influenced the interaction experience more than any other considered predictor variable. To study closeness more rigorously as the central manipulation, we conducted Study 2, a between-participants experiment with 24 pairs (N = 48) and a similar procedure. Robot operators who reported a closer relationship with their teammate felt more present in this investigation. These findings can inform the design and application of telepresence robot systems to increase a remote operator's feelings of presence via robot.

Since robots are increasingly expected to work in concert with humans in dynamic, unstructured environments, they will need to express information about their state and actions. We propose a formalism for planning robot communication that employs a probabilistic representation of the robots world. This representation takes on the form of a Markov Decision Process (MDP) and captures the uncertainty of interacting with humans. The key insight of this work is that humans preferences and time need to be carefully balanced against the robots in order to minimize human annoyance. The communication-MDP enables the robot to reason about the effects of its actions on a human interactor. We validated the model through a human subjects experiment (n=44) by learning communication policies for a loosely collaborative task. The results show that the communication-MDP improves participants' perceptions of the robot's thoughtfulness as an interactor.

Auditory cues facilitate situational awareness by enabling humans to infer what is happening in the nearby environment. Unlike humans, many robots do not continuously produce perceivable state-expressive sounds. In this work, we propose the use of iconic auditory signals that mimic the sounds produced by a robot»s operations. In contrast to artificial sounds (e.g., beeps and whistles), these signals are primarily functional, providing information about the robot»s actions and state. We analyze the effects of two variations of robot sound, tonal and broadband, on auditory localization during a human-robot collaboration task. Results from 24 participants show that both signals significantly improve auditory localization, but the broadband variation is preferred by participants. We then present a computational formulation for auditory signaling and apply it to the problem of auditory localization using a human-subjects data collection with 18 participants to learn optimal signaling policies.

Recent work has shown the potential for lights to act as simple, yet expressive signaling mechanisms for use in a variety of human-robot applications. However, the wide range of robot shapes and sizes makes it difficult for researchers to quickly prototype and evaluate different light configurations and signal designs. In this work, we present the design of ModLight, a modular research tool consisting of a set of low cost light blocks that can be easily reconfigured to fit a myriad of robots and applications. ModLight also provides researchers, designers, and students with open-source software tools that enable them to visually design new signals and easily integrate them into existing systems. This work also serves to motivate the need for further research in developing light behaviors for use in human-robot interaction. Towards this goal, we present our design rationale including a brief analysis of the signaling needs of several robots and applications.

Telepresence robots have the potential to improve access to K-12 education for students who are unable to attend school for a variety of reasons. Since previous telepresence research has largely focused on the needs of adult users in workplace settings, it is unknown what challenges must be addressed for these robots to be effective tools in classrooms. In this paper, we seek to better understand how a telepresence robot should function in the classroom when operated by a remote student. Toward this goal, we conducted field sessions in which four designers operated a telepresence robot in a real K-12 classroom. Using the results, we identify key research challenges and present design insights meant to inform the HRI community in particular and robot designers in general.

Telepresence robots have the potential to improve access to K-12 education. However, designing robots for classroom use presents unique challenges from both logistical and technological perspectives. To address these challenges, we created My Classroom Robot, an interactive game in which players can operate a virtual telepresence robot in a classroom environment. The virtual classroom environment allows us to collect data and prototype different designs prior to involving the high overhead required in going into the real classroom. In this work, we present the design of My Classroom Robot, an initial evaluation, and the lessons learned from its development.

Non-humanoid robots are becoming increasingly utilized for collaborative tasks across many domains, including industrial and service settings. Collaborative tasks between the human and robot rely on each collaborator's ability to effectively convey their mental state while accurately estimating and interpreting their partner's knowledge, intent, and actions. My research focuses on nonverbal communication signals that a non-humanoid robot can utilize during human-robot collaboration. We focus on motion, light and sound as they are commonly used communication channels across many domains and are available on most robot platforms. As a first step towards this goal, I present a completed study exploring how to use a simple multimodal light and sound signal to request help during a collaborative task. We then discuss future work to generate and utilize more complex signals to convey a variety of statuses to improve collaboration.

Non-humanoid robots are becoming increasingly utilized for collaborative tasks that rely on each collaborator's ability to effectively convey their mental state while accurately estimating and interpreting their partner's knowledge, intent, and actions. During these tasks, it may be beneficial or even necessary for the human collaborator to assist the robot. Consequently, we explore the use of nonverbal signals to request help during a collaborative task. We focus on light and sound as they are commonly used communication channels across many domains. This paper analyzes the effectiveness of three nonverbal help signals that vary in urgency. Our results show that these signals significantly influence the human collaborator's and their perception of the collaboration.

In the future, we envision domestic robots to play a large role in our everyday lives. This requires robots able to anticipate our needs and preferences and adapt their behavior. Since current robotics research takes place primarily in laboratory settings, it fails to take into account real users. In this work, we explore how organization occurs in the kitchen through a home study. Our analysis includes qualitative insights towards robot behavior during kitchen organization, an open source dataset of real life kitchens, and a proof-of-concept application of this dataset to the problem of object return.

Robotics research often focuses on increasing robot capability. If end users do not perceive these increases, however, user acceptance may not improve. In this work, we explore the idea of perceived capability and how it relates to true capability, differentiating between physical and social capabilities. We present a framework that outlines their potential relationships, along with two user studies, on robot speed and speech, exploring these relationships. Our studies identify two possible consequences of the disconnect between the true and perceived capability: (1) under-perception: true improvements in capability may not lead to perceived improvements and (2) over-perception: true improvements in capability may lead to additional perceived improvements that do not actually exist.

The success of a Learning from Demonstration system depends on the quality of the demonstrated data. Kinesthetic demonstrations are often assumed to be the best method of providing demonstrations for manipulation tasks, however, there is little research to support this. In this work, we explore the use of a simulated environment as an alternative to and in combination with kinesthetic demonstrations when using an autonomous dynamical system to encode motion. We present the results of a user study comparing three demonstrations interfaces for a manipulation task on a KUKA LWR robot.

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No abstract available.