ICRA 2011 Paper Abstract

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Paper WeA211.1

Jiang, Yun (Cornell University), Stephen, Moseson (Cornell University), Saxena, Ashutosh (Cornell University)

Efficient Grasping from RGBD Images: Learning Using a New Rectangle Representation

Scheduled for presentation during the Regular Sessions "Grasping II" (WeA211), Wednesday, May 11, 2011, 10:05−10:20, Room 5F

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords Grasping, Visual Learning, Learning and Adaptive Systems

Abstract

Given an image and an aligned depth map of an object, our goal is to estimate the full seven dimensional gripper configuration---its 3D location, 3D orientation and the gripper opening width. Recently, learning algorithms have been successfully applied to grasp novel objects---ones not seen by the robot before. While these approaches use low-dimensional representations such as a `grasping point' or a `pair of points' that are perhaps easier to learn, they only partly represent the gripper configuration and hence are sub-optimal.

We propose to learn a new `grasping rectangle' representation: an oriented rectangle in the image plane. It takes into account the location, the orientation as well as the gripper opening width. However, inference with such a representation is computationally expensive. In this work, we present a two step process in which the first step prunes the search space efficiently using certain features that are fast to compute. For the remaining few cases, the second step uses advanced features to accurately select a good grasp. In our extensive experiments, we show that our robot successfully uses our algorithm to pick up a variety of novel objects.

 

 

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