Date of Original Version

10-2010

Type

Conference Proceeding

Abstract or Description

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.

DOI

10.1109/IROS.2010.5650464

Included in

Robotics Commons

Share

COinS
 

Published In

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2117-2122.