  We present an ensemble learning methodology that combines multiple existing
robotic grasp synthesis algorithms and obtain a success rate that is
significantly better than the individual algorithms. The methodology treats the
grasping algorithms as "experts" providing grasp "opinions". An Ensemble
Convolutional Neural Network (ECNN) is trained using a Mixture of Experts (MOE)
model that integrates these opinions and determines the final grasping
decision. The ECNN introduces minimal computational cost overhead, and the
network can virtually run as fast as the slowest expert. We test this
architecture using open-source algorithms in the literature by adopting GQCNN
4.0, GGCNN and a custom variation of GGCNN as experts and obtained a 6%
increase in the grasp success on the Cornell Dataset compared to the
best-performing individual algorithm. The performance of the method is also
demonstrated using a Franka Emika Panda arm.
