This paper presents the software framework established to facilitate cloud-hosted robot simulation. The framework addresses the challenges associated with conducting a task-oriented and real-time robot competition, the Defense Advanced Research Projects Agency (DARPA) Virtual Robotics Challenge (VRC), designed to mimic reality. The core of the framework is the Gazebo simulator, a platform to simulate robots, objects, and environments, as well as the enhancements made for the VRC to maintain a high fidelity simulation using a high degree of freedom and multisensor robot. The other major component used is the CloudSim tool, designed to enhance the automation of robotics simulation using existing cloud technologies. The results from the VRC and a discussion are also detailed in this work.

Managing user expectations of personal robots becomes particularly challenging when the end-user just wants to know what the robot can do, and neither understands nor cares about its technical specifications. In describing what a robot can do to such an end-user, we explored the questions of (a) whether or not such users would respond to expectation setting about personal robots and, if so, (b) how such expectation setting would influence human-robot interactions and people's perceptions of the robots. Using a 2 (expectation setting: high vs. low) x 2 (robot type: Pleo vs. AIBO) between-participants experiment (N=24), we examined these questions. We found that people's initial beliefs about the robot's capabilities are indeed influenced by expectation setting tactics. Contrary to the hypotheses predicted by the Self-Fulfilling Prophecy and Confirmation Bias, we found that erring on the side of setting expectations lower rather than higher led to less disappointment and more positive appraisals of the robot's competence.