Model-Free Grasp Learning Framework based on Physical Simulation
The work at hand presents a generic framework to build classifiers that allow to predict the quality of 6-DOF grasp candidates for arbitrary mechanical grippers based on the depth data captured by a depth sensor. Hereby, the framework covers the whole process of setting up a deep neural network for a given mechanical gripper by making use of synthetic data resulting from a new grasp simulation tool. Furthermore, a new extended convolutional neural network (CNN) architecture is introduced that estimates the quality of a suggested grasp candidate based on local depth information and the pose of the corresponding grasp. As a result, robust grasp candidates can be detected in a model-free fashion.
Riedlinger, Marc A.
Khalid, Muhammad Usman