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.

It is challenging to quantitatively measure a user's trust in a robot system using traditional survey methods due to their invasiveness and tendency to disrupt the flow of operation. Therefore, we analyzed data from an existing experiment to identify measures which (1) have face validity for measuring trust and (2) align with the collected post-run trust measures. Two measures are promising as real-time indications of a drop in trust. The first is the time between the most recent warning and when the participant reduces the robot's autonomy level. The second is the number of warnings prior to the reduction of the autonomy level.

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.

Controlling the movements of mobile robots, including driving the robot through the world and panning the robot's cameras, typically requires many physical joysticks, buttons, and switches. Operators will often employ a technique called "chording" to cope with this situation. Much like a piano player, the operator will simultaneously actuate multiple joysticks and switches with his or her hands to create a combination of complimentary movements. However, these controls are in fixed locations and unable to be reprogrammed easily. Using a Microsoft Surface multi-touch table, we have designed an interface that allows chording and simultaneous multi-handed interaction anywhere that the user wishes to place his or her hands. Taking inspiration from the biomechanics of the human hand, we have created a dynamically resizing, ergonomic, and multi-touch controller (the DREAM Controller). This paper presents the design and testing of this controller with an iRobot ATRV-JR robot.

No abstract available.