Abstract
AbstractGrasp estimation is a fundamental technique crucial for robot manipulation tasks. In this work, we present a scene-oriented grasp estimation scheme taking constraints of the grasp pose imposed by the environment into consideration and training on samples satisfying the constraints. We formulate valid grasps for a parallel-jaw gripper as vectors in a two-dimensional (2D) image and detect them with a fully convolutional network that simultaneously estimates the vectors’ origins and directions. The detected vectors are then converted to 6 degree-of-freedom (6-DOF) grasps with a tailored strategy. As such, the network is able to detect multiple grasp candidates from a cluttered scene in one shot using only an RGB image as input. We evaluate our approach on the GraspNet-1Billion dataset and archived comparable performance as state-of-the-art while being efficient in runtime.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Cited by
6 articles.
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