  Robot-assisted bite acquisition involves picking up food items that vary in
their shape, compliance, size, and texture. A fully autonomous strategy for
bite acquisition is unlikely to efficiently generalize to this wide variety of
food items. We propose to leverage the presence of the care recipient to
provide feedback when the system encounters novel food items. However,
repeatedly asking for help imposes cognitive workload on the user. In this
work, we formulate human-in-the-loop bite acquisition within a contextual
bandit framework and propose a novel method, LinUCB-QG, that selectively asks
for help. This method leverages a predictive model of cognitive workload in
response to different types and timings of queries, learned using data from 89
participants collected in an online user study. We demonstrate that this method
enhances the balance between task performance and cognitive workload compared
to autonomous and querying baselines, through experiments in a food
dataset-based simulator and a user study with 18 participants without mobility
limitations.
