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. Note to Practitioners-Advances in robot simulation, cloud hosted infrastructure, and web technology have made it possible to accurately and efficiently simulate complex robots and environments on remote servers while providing realistic data streams for human-in-the-loop robot control. This paper presents the software and hardware frameworks established to facilitate cloud-hosted robot simulation, and addresses the challenges associated with conducting a task-oriented robot competition designed to mimic reality. The competition that spurred this innovation was the VRC, a precursor to the DARPA Robotics Challenge, in which teams from around the world utilized custom human-robot interfaces and control code to solve disaster response-related tasks in simulation. Winners of the VRC received both funding and access to Atlas, a humanoid robot developed by Boston Dynamics. The Gazebo simulator, an open source and high fidelity robot simulator, was improved upon to met the needs of the VRC competition. Additionally, CloudSim was created to act as an interface between users and the cloud-hosted simulations.

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Index Terms-HRI, mobile user interface, information theory I. RESEARCH PROBLEM AND A PROPOSALThe communication bottleneck between robots and people [1] presents an enormous challenge to the human-robot interaction community. Rather than exclusively focusing on improving robot object learning, task learning, and natural language understanding, we propose also designing interfaces that make up for low communication bandwidth by thoughtfully accounting for the constrained capabilities of robots [2].People are adept at compensating for communication limitations, changing their communicative strategies for talking to pets, babies [3], foreigners [4], and robots [5]. Communicative accommodation already exists. Thus, instead of requiring robots to perfectly understand natural language, gestures, etc., there is a wide variety of research and design to be done in the space of alternative communicative modalities.We propose to approach this problem by accounting for limitations in robot abilities and taking advantage of already familiar human-computer interaction models, leveraging a communication model based upon Information Theory. Using this design perspective, we present three different mobile user interfaces that were fully developed and implemented on a PR2 (Personal Robot 2) [6] for task domains in navigation, perception, learning and manipulation. II. RELEVANT THEORIESWe can observe parallels between human robot interaction and the interaction between humans and general complex autonomous systems. Sheridan's taxonomy of complex human-machine systems describes the following sequence of operations: (1) acquire information, (2) analyze and display information, (3) decide on an action, and (4) implement that action [7, p. 61]. This provides the groundwork for identifying the stages at which people and/or robots should lead. In the current projects, the personal robot autonomously completes steps 1, 2 and 4, and the person completes step 3. Thus, the user interface design must address how the robot analyzes and displays its sensor information and world model to the human, and how the human can effectively communicate desired actions to the robot. An analysis of our case studies in Sheridan's framework is displayed in Fig. 1. All of our systems use the trading model of alternately passing control back and forth between human and robot, as opposed to the sharing model of simultaneous control described in [7, p. 63].Gold proposed using an Information Pipeline model for HRI that is based upon information theory [8], a mathematical model of communication developed for quantifying the amount of information that could be transported through a given channel. Schramm [9] developed a theory of communication that put these ideas into the context of two-way joint communications. This could be helpful when considering the large amount of overhead involved in encoding and decoding messages sent between people and robots.The focus of the projects in this paper was on designing interfaces that applied this theory to human-robot commun...

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