Abstract-Prior work in human trust of autonomous robots suggests the timing of reliability drops impact trust and control allocation strategies. However, trust is traditionally measured post-run, thereby masking the real-time changes in trust, reducing sensitivity to factors like inertia, and subjecting the measure to biases like the primacy-recency effect. Likewise, little is known on how feedback of robot confidence interacts in real-time with trust and control allocation strategies. An experiment to examine these issues showed trust loss due to early reliability drops is masked in traditional post-run measures, trust demonstrates inertia, and feedback alters allocation strategies independent of trust. The implications of specific findings on development of trust models and robot design are also discussed.

Prior work in human-autonomy interaction has focused on plant systems that operate in highly structured environments. In contrast, many human-robot interaction (HRI) tasks are dynamic and unstructured, occurring in the open world. It is our belief that methods developed for the measurement and modeling of trust in traditional automation need alteration in order to be useful for HRI. Therefore, it is important to characterize the factors in HRI that influence trust. This study focused on the influence of changing autonomy reliability. Participants experienced a set of challenging robot handling scenarios that forced autonomy use and kept them focused on autonomy performance. The counterbalanced experiment included scenarios with different low reliability windows so that we could examine how drops in reliability altered trust and use of autonomy. Drops in reliability were shown to affect trust, the frequency and timing of autonomy mode switching, as well as participants' self-assessments of performance. A regression analysis on a number of robot, personal, and scenario factors revealed that participants tie trust more strongly to their own actions rather than robot performance.

Telepresence robots can be thought of as embodied video conferencing on wheels. Companies producing these robots imagine them being used in a wide variety of situations (e.g., ad-hoc conversations at the office, inspections and troubleshooting at factories, and patient rounds at medical facilities). In July and August 2010, we examined office-related use cases in a series of studies using two prototype robots (Anybots' QB and VGo Communications' VGo). In this paper, we present two studies: conference room meetings (n=6) and moving hallway conversations (n=24). We discuss who might benefit from using telepresence robots, in what scenarios, and the features that telepresence robots must incorporate for use in ad-hoc interactions.

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-Most robot systems have discrete autonomy levels, if they possess more than a single autonomy level. A user or the robot may switch between these discrete modes, but the robot can not operate at a level between any two modes. We have developed a sliding scale autonomy system that allows autonomy levels to be created and changed on the fly. This paper discusses the system's architecture and presents the results of experiments with the sliding scale autonomy system.

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Multi-touch technologies hold much promise for the command and control of mobile robot teams. To improve the ease of learning and usability of these interfaces, we conducted an experiment to determine the gestures that people would naturally use, rather than the gestures they would be instructed to use in a pre-designed system. A set of 26 tasks with differing control needs were presented sequentially on a DiamondTouch to 31 participants. We found that the task of controlling robots exposed unique gesture sets and considerations not previously observed, particularly in desktop-like applications. In this paper, we present the details of these findings, a taxonomy of the gesture set, and guidelines for designing gesture sets for robot control.

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