People Helping Robots Helping People: Crowdsourcing for Grasping Novel Objects

Alexander Sorokin, Dmitry Berenson, Siddhartha Srinivasa, and Martial Hebert
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '10), October, 2010.


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Abstract
For successful deployment, personal robots must adapt to ever-changing indoor environments. While dealing with novel objects is a largely unsolved challenge in AI, it is easy for people. In this paper we present a framework for robot supervision through Amazon Mechanical Turk. Unlike traditional models of teleoperation, people provide semantic information about the world and subjective judgements. The robot then autonomously utilizes the additional information to enhance its capabilities. The information can be collected on demand in large volumes and at low cost. We demonstrate our approach on the task of grasping unknown objects.

Keywords
crowdsourcing, computer vision, object modeling, grasping

Notes
Associated Center(s) / Consortia: Quality of Life Technology Center

Text Reference
Alexander Sorokin, Dmitry Berenson, Siddhartha Srinivasa, and Martial Hebert, "People Helping Robots Helping People: Crowdsourcing for Grasping Novel Objects," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '10), October, 2010.

BibTeX Reference
@inproceedings{Sorokin_2010_6757,
   author = "Alexander Sorokin and Dmitry Berenson and Siddhartha Srinivasa and Martial Hebert",
   title = "People Helping Robots Helping People: Crowdsourcing for Grasping Novel Objects",
   booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '10)",
   month = "October",
   year = "2010",
}