Deep Learning for Detecting Robotic Grasps

From Ian Lenz, Honglak Lee, Ashutosh Saxena:


We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast and robust, we present a two-step cascaded system with two deep networks, where the top detections from the first are re-evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs effectively, for which we present a method that applies structured regularization on the weights based on multimodal group regularization. We show that our method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms... (homepage) (full pdf paper)

Comments (0)

This post does not have any comments. Be the first to leave a comment below.

Post A Comment

You must be logged in before you can post a comment. Login now.

Featured Product

2nd RoboDEX - Robot Development & Application EXPO

2nd RoboDEX - Robot Development & Application EXPO

RoboDEX, a comprehensive trade show for robots, will be held at the center of robot/robotics industry, Tokyo, 2018. Covering from development technology of robots to application of robots, it attracts all the professionals involved in robot industry and professionals considering utilizing robots.