  Robot-assisted feeding has the potential to improve the quality of life for
individuals with mobility limitations who are unable to feed themselves
independently. However, there exists a large gap between the homogeneous,
curated plates existing feeding systems can handle, and truly in-the-wild
meals. Feeding realistic plates is immensely challenging due to the sheer range
of food items that a robot may encounter, each requiring specialized
manipulation strategies which must be sequenced over a long horizon to feed an
entire meal. An assistive feeding system should not only be able to sequence
different strategies efficiently in order to feed an entire meal, but also be
mindful of user preferences given the personalized nature of the task. We
address this with FLAIR, a system for long-horizon feeding which leverages the
commonsense and few-shot reasoning capabilities of foundation models, along
with a library of parameterized skills, to plan and execute user-preferred and
efficient bite sequences. In real-world evaluations across 6 realistic plates,
we find that FLAIR can effectively tap into a varied library of skills for
efficient food pickup, while adhering to the diverse preferences of 42
participants without mobility limitations as evaluated in a user study. We
demonstrate the seamless integration of FLAIR with existing bite transfer
methods [19, 28], and deploy it across 2 institutions and 3 robots,
illustrating its adaptability. Finally, we illustrate the real-world efficacy
of our system by successfully feeding a care recipient with severe mobility
limitations. Supplementary materials and videos can be found at:
https://emprise.cs.cornell.edu/flair .
