ICRA 2012 Paper Abstract

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Paper TuD09.3

Madry, Marianna (Royal Institute of Technology (KTH), Sweden), Song, Dan (Royal Inst. of Tech. (KTH), Stockholm), Kragic, Danica (KTH)

From Object Categories to Grasp Transfer Using Probabilistic Reasoning

Scheduled for presentation during the Regular Session "Sensing for manipulation" (TuD09), Tuesday, May 15, 2012, 17:00−17:15, Meeting Room 9 (Sa)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 11, 2017

Keywords Recognition, Grasping, Computer Vision for Robotics and Automation

Abstract

In this paper we address the problem of grasp generation and grasp transfer between objects using categorical knowledge. The system is built upon an i)~active scene segmentation module, able of generating object hypotheses and segmenting them from the background in real time, ii)~object categorization system using integration of 2D and 3D cues, and iii)~probabilistic grasp reasoning system. Individual object hypotheses are first generated, categorized and then used as the input to a grasp generation and transfer system that encodes task, object and action properties. The experimental evaluation compares individual 2D and 3D categorization approaches with the integrated system, and it demonstrates the usefulness of the categorization in task-based grasping and grasp transfer.

 

 

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