  In this paper, we introduce two methods of improving real-time object
grasping performance from monocular colour images in an end-to-end CNN
architecture. The first is the addition of an auxiliary task during model
training (multi-task learning). Our multi-task CNN model improves grasping
performance from a baseline average of 72.04% to 78.14% on the large Jacquard
grasping dataset when performing a supplementary depth reconstruction task. The
second is introducing a positional loss function that emphasises loss per pixel
for secondary parameters (gripper angle and width) only on points of an object
where a successful grasp can take place. This increases performance from a
baseline average of 72.04% to 78.92% as well as reducing the number of training
epochs required. These methods can be also performed in tandem resulting in a
further performance increase to 79.12% while maintaining sufficient inference
speed to afford real-time grasp processing.
