  In this study, we introduce a novel visual imitation network with a spatial
attention module for robotic assisted feeding (RAF). The goal is to acquire
(i.e., scoop) food items from a bowl. However, achieving robust and adaptive
food manipulation is particularly challenging. To deal with this, we propose a
framework that integrates visual perception with imitation learning to enable
the robot to handle diverse scenarios during scooping. Our approach, named AVIL
(adaptive visual imitation learning), exhibits adaptability and robustness
across different bowl configurations in terms of material, size, and position,
as well as diverse food types including granular, semi-solid, and liquid, even
in the presence of distractors. We validate the effectiveness of our approach
by conducting experiments on a real robot. We also compare its performance with
a baseline. The results demonstrate improvement over the baseline across all
scenarios, with an enhancement of up to 2.5 times in terms of a success metric.
Notably, our model, trained solely on data from a transparent glass bowl
containing granular cereals, showcases generalization ability when tested
zero-shot on other bowl configurations with different types of food.
