  Robotic grasping for a diverse set of objects is essential in many robot
manipulation tasks. One promising approach is to learn deep grasping models
from large training datasets of object images and grasp labels. However,
empirical grasping datasets are typically sparsely labeled (i.e., a small
number of successful grasp labels in each image). The data sparsity issue can
lead to insufficient supervision and false-negative labels and thus results in
poor learning results. This paper proposes a Maximum Likelihood Grasp Sampling
Loss (MLGSL) to tackle the data sparsity issue. The proposed method supposes
that successful grasps are stochastically sampled from the predicted grasp
distribution and maximizes the observing likelihood. MLGSL is utilized for
training a fully convolutional network that generates thousands of grasps
simultaneously. Training results suggest that models based on MLGSL can learn
to grasp with datasets composing of 2 labels per image. Compared to previous
works, which require training datasets of 16 labels per image, MLGSL is 8x more
data-efficient. Meanwhile, physical robot experiments demonstrate an equivalent
performance at a 90.7% grasp success rate on household objects.
