  Autonomous feeding is challenging because it requires manipulation of food
items with various compliance, sizes, and shapes. To understand how humans
manipulate food items during feeding and to explore ways to adapt their
strategies to robots, we collected a rich dataset of human trajectories by
asking them to pick up food and feed it to a mannequin. From the analysis of
the collected haptic and motion signals, we demonstrate that humans adapt their
control policies to accommodate to the compliance and shape of the food item
being acquired. We propose a taxonomy of manipulation strategies for feeding to
highlight such policies. As a first step to generate compliance-dependent
policies, we propose a set of classifiers for compliance-based food
categorization from haptic and motion signals. We compare these human
manipulation strategies with fixed position-control policies via a robot. Our
analysis of success and failure cases of human and robot policies further
highlights the importance of adapting the policy to the compliance of a food
item.
