The Human-Robot Cloud: Situated collective intelligence on demand

The Human-Robot Cloud (HRC) is an innovative extension of Cloud Computing across two important directions: First, while traditional cloud computing enables transparent utilization of distributed computational as well as storage resources, the HRC enables, in addition to the above two, the utilization of (a) distributed sensing (sensor network technology) and (b) actuator networks (including robot networks). Thus, HRC extends the concept of cloud computing by connecting it to the “Physical World”, through sensing and action. Second, while traditional cloud computing involves the usage of only electronic components, such as computers and storage devices, the HRC's capability is extended by the support of human physical and cognitive “components” as part of the cloud, which are neither expected to be experts nor to be engaged with the cloud full-time. Such components are primarily expected to interact with the system for only short periods of time (seconds), essentially providing crowd-servicing for the Cloud. Human components provide any or a mixture of the following: a) input arising from a number of sources through the usage of their sensory faculties (auditory, visual etc.), - thus, acting as “intelligent sensors” attached to the cloud; b) input that results from the usage of their cognitive faculties (pattern recognition, prediction, identification, planning etc.) - thus, acting as “intelligent systems” attached to the cloud; and c) actuation services to the Cloud (by moving around their bodies or other objects) - thus acting as “actuators” attached to the cloud. Thus, the proposed HRC is aiming to achieve the best of both worlds, i.e., either humans or machines, being able to carry out tasks which are very difficult or impossible for either humans or machines alone to carry out. Furthermore, the HRC enables the construction of situated agents exhibiting collective intelligence on demand, and the transformation of situated agency from a “capital investment” to a service, components of which can be provided by multiple providers, in a transparent fashion to the end user.

[1]  Hans Thies,et al.  Quality Assurance for Human-Based Electronic Services: A Decision Matrix for Choosing the Right Approach , 2010, ICWE Workshops.

[2]  Chris Callison-Burch,et al.  Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk , 2009, EMNLP.

[3]  Luis von Ahn Games with a Purpose , 2006, Computer.

[4]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[5]  K. Haase,et al.  Automated discovery , 1988 .

[6]  John C. Tang,et al.  Reflecting on the DARPA Red Balloon Challenge , 2011, Commun. ACM.

[7]  Hai Liu,et al.  Localized Movement Control for Fault Tolerance of Mobile Robot Networks , 2007, WSAN.

[8]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[9]  Rajkumar Buyya,et al.  Market-Oriented Cloud Computing: Vision, Hype, and Reality of Delivering Computing as the 5th Utility , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[10]  J. Mixter Fast , 2012 .

[11]  Xiaomeng Su,et al.  A Survey of Automated Web Service Composition Methods , 2004, SWSWPC.

[12]  Francesco M. Donini,et al.  Fully Automated Web Services Discovery and Composition Through Concept Covering and Concept Abduction , 2007, Int. J. Web Serv. Res..

[13]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[14]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[15]  P M Asaro,et al.  Remote-Control Crimes , 2011, IEEE Robotics & Automation Magazine.

[16]  David A. Forsyth,et al.  Utility data annotation with Amazon Mechanical Turk , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[17]  Manuel Blum,et al.  reCAPTCHA: Human-Based Character Recognition via Web Security Measures , 2008, Science.

[18]  Anupriya Ankolekar,et al.  Automated discovery, interaction and composition of Semantic Web services , 2003, J. Web Semant..

[19]  Michael S. Bernstein,et al.  Crowds in two seconds: enabling realtime crowd-powered interfaces , 2011, UIST.

[20]  Aniket Kittur,et al.  Crowdsourcing user studies with Mechanical Turk , 2008, CHI.

[21]  Michael Kaminsky,et al.  SybilGuard: defending against sybil attacks via social networks , 2006, SIGCOMM.

[22]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[23]  Vijay Kumar,et al.  Simultaneous Coverage and Tracking (SCAT) of Moving Targets with Robot Networks , 2008, WAFR.

[24]  M. Banerjee,et al.  Beyond kappa: A review of interrater agreement measures , 1999 .

[25]  Nikolaos Papanikolopoulos,et al.  Dispersion behaviors for a team of multiple miniature robots , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[26]  Prithwish Basu,et al.  Movement control algorithms for realization of fault-tolerant ad hoc robot networks , 2004, IEEE Network.

[27]  G. Veruggio The birth of roboethics , 2005 .

[28]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[29]  Haoqi Zhang,et al.  An Iterative Dual Pathway Structure for Speech-to-Text Transcription , 2011, Human Computation.

[30]  Kurt Geihs,et al.  Different Approaches to Semantic Web Service Composition , 2008, 2008 Third International Conference on Internet and Web Applications and Services.

[31]  Nikolaos Papanikolopoulos,et al.  Heterogeneous implementation of an adaptive robotic sensing team , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[32]  Vijay Kumar,et al.  Robot and sensor networks for first responders , 2004, IEEE Pervasive Computing.

[33]  Cliff Changchun Zou,et al.  iCAPTCHA: The Next Generation of CAPTCHA Designed to Defend against 3rd Party Human Attacks , 2011, 2011 IEEE International Conference on Communications (ICC).

[34]  Mike P. Papazoglou,et al.  Web Services - Principles and Technology , 2007 .

[35]  Eric Horvitz,et al.  Task routing for prediction tasks , 2012, AAMAS.

[36]  Ian F. Akyildiz,et al.  Wireless Sensor Networks: Akyildiz/Wireless Sensor Networks , 2010 .

[37]  Wei Hong,et al.  TinyDB: an acquisitional query processing system for sensor networks , 2005, TODS.

[38]  Laura A. Dabbish,et al.  Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[39]  Eugene Ciurana,et al.  Developing with Google App Engine , 2009 .

[40]  Rajkumar Buyya,et al.  Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.