FeCCM for Scene Understanding: Helping the Robot to Learn Multiple Tasks
Helping a robot to understand a scene can include many sub-tasks, such as scene categorization, object detection, geometric labeling, etc. Each sub-task is notoriously hard, and state-of-art classifiers exist for many sub-tasks. It is desirable to have an algorithm that can capture such correlation without requiring to make any changes to the inner workings of any classifier, and therefore make the perception for a robot better. We have recently proposed a generic model (Feedback Enabled Cascaded Classification Model) that enables us to easily take state-of-art classifiers as black-boxes and improve performance. In this video, we show that we can use our FeCCM model to quickly combine existing classifiers for various sub-tasks, and build a shoe finder robot in a day. The video shows our robot using FeCCM to find a shoe on request.
Congcong Li TP Wong Norris Xu Ashutosh Saxena
Department of Electrical and Computer Engineering,Cornell University,Ithaca,NY 14853,USA Department of Computer Science,Cornell University,Ithaca,NY 14853,USA
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
3449-3450
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）