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- Publisher
- Springer Journals
- Copyright
- Copyright © 2015 by Springer Science+Business Media New York
- Subject
- Engineering; Robotics and Automation; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
- ISSN
- 0929-5593
- eISSN
- 1573-7527
- DOI
- 10.1007/s10514-015-9451-2
- Publisher site
- See Article on Publisher Site
Abstract
Object recognition and manipulation are critical for enabling robots to operate in household environments. Many grasp planners can estimate grasps based on object shape, but they ignore key information about non-visual object characteristics. Object model databases can account for this information, but existing methods for database construction are time and resource intensive. We present an easy-to-use system for constructing object models for 3D object recognition and manipulation made possible by advances in web robotics. The database consists of point clouds generated using a novel iterative point cloud registration algorithm. The system requires no additional equipment beyond the robot, and non-expert users can demonstrate grasps through an intuitive web interface. We validate the system with data collected from both a crowdsourcing user study and expert demonstration. We show that the demonstration approach outperforms purely vision-based grasp planning approaches for a wide variety of object classes.
Journal
Autonomous Robots – Springer Journals
Published: Jul 10, 2015