  The robotic handling of compliant and deformable food raw materials,
characterized by high biological variation, complex geometrical 3D shapes, and
mechanical structures and texture, is currently in huge demand in the ocean
space, agricultural, and food industries. Many tasks in these industries are
performed manually by human operators who, due to the laborious and tedious
nature of their tasks, exhibit high variability in execution, with variable
outcomes. The introduction of robotic automation for most complex processing
tasks has been challenging due to current robot learning policies. A more
consistent learning policy involving skilled operators is desired. In this
paper, we address the problem of robot learning when presented with
inconsistent demonstrations. To this end, we propose a robust learning policy
based on Learning from Demonstration (LfD) for robotic grasping of food
compliant objects. The approach uses a merging of RGB-D images and tactile data
in order to estimate the necessary pose of the gripper, gripper finger
configuration and forces exerted on the object in order to achieve effective
robot handling. During LfD training, the gripper pose, finger configurations
and tactile values for the fingers, as well as RGB-D images are saved. We
present an LfD learning policy that automatically removes inconsistent
demonstrations, and estimates the teacher's intended policy. The performance of
our approach is validated and demonstrated for fragile and compliant food
objects with complex 3D shapes. The proposed approach has a vast range of
potential applications in the aforementioned industry sectors.
