  A robotic feeding system must be able to acquire a variety of foods. Prior
bite acquisition works consider single-arm spoon scooping or fork skewering,
which do not generalize to foods with complex geometries and deformabilities.
For example, when acquiring a group of peas, skewering could smoosh the peas
while scooping without a barrier could result in chasing the peas on the plate.
In order to acquire foods with such diverse properties, we propose stabilizing
food items during scooping using a second arm, for example, by pushing peas
against the spoon with a flat surface to prevent dispersion. The added
stabilizing arm can lead to new challenges. Critically, this arm should
stabilize the food scene without interfering with the acquisition motion, which
is especially difficult for easily breakable high-risk food items like tofu.
These high-risk foods can break between the pusher and spoon during scooping,
which can lead to food waste falling out of the spoon. We propose a general
bimanual scooping primitive and an adaptive stabilization strategy that enables
successful acquisition of a diverse set of food geometries and physical
properties. Our approach, CARBS: Coordinated Acquisition with Reactive Bimanual
Scooping, learns to stabilize without impeding task progress by identifying
high-risk foods and robustly scooping them using closed-loop visual feedback.
We find that CARBS is able to generalize across food shape, size, and
deformability and is additionally able to manipulate multiple food items
simultaneously. CARBS achieves 87.0% success on scooping rigid foods, which is
25.8% more successful than a single-arm baseline, and reduces food breakage by
16.2% compared to an analytical baseline. Videos can be found at
https://sites.google.com/view/bimanualscoop-corl22/home .
