  Autonomous robot-assisted feeding requires the ability to acquire a wide
variety of food items. However, it is impossible for such a system to be
trained on all types of food in existence. Therefore, a key challenge is
choosing a manipulation strategy for a previously unseen food item. Previous
work showed that the problem can be represented as a linear bandit with visual
context. However, food has a wide variety of multi-modal properties relevant to
manipulation that can be hard to distinguish visually. Our key insight is that
we can leverage the haptic context we collect during and after manipulation
(i.e., "post hoc") to learn some of these properties and more quickly adapt our
visual model to previously unseen food. In general, we propose a modified
linear contextual bandit framework augmented with post hoc context observed
after action selection to empirically increase learning speed and reduce
cumulative regret. Experiments on synthetic data demonstrate that this effect
is more pronounced when the dimensionality of the context is large relative to
the post hoc context or when the post hoc context model is particularly easy to
learn. Finally, we apply this framework to the bite acquisition problem and
demonstrate the acquisition of 8 previously unseen types of food with 21% fewer
failures across 64 attempts.
