LAVA: Long-horizon Visual Action based Food Acquisition
arxiv(2024)
摘要
Robotic Assisted Feeding (RAF) addresses the fundamental need for individuals
with mobility impairments to regain autonomy in feeding themselves. The goal of
RAF is to use a robot arm to acquire and transfer food to individuals from the
table. Existing RAF methods primarily focus on solid foods, leaving a gap in
manipulation strategies for semi-solid and deformable foods. This study
introduces Long-horizon Visual Action (LAVA) based food acquisition of liquid,
semisolid, and deformable foods. Long-horizon refers to the goal of "clearing
the bowl" by sequentially acquiring the food from the bowl. LAVA employs a
hierarchical policy for long-horizon food acquisition tasks. The framework uses
high-level policy to determine primitives by leveraging ScoopNet. At the
mid-level, LAVA finds parameters for primitives using vision. To carry out
sequential plans in the real world, LAVA delegates action execution which is
driven by Low-level policy that uses parameters received from mid-level policy
and behavior cloning ensuring precise trajectory execution. We validate our
approach on complex real-world acquisition trials involving granular, liquid,
semisolid, and deformable food types along with fruit chunks and soup
acquisition. Across 46 bowls, LAVA acquires much more efficiently than
baselines with a success rate of 89 +/- 4
plate variations such as different positions, varieties, and amount of food in
the bowl. Code, datasets, videos, and supplementary materials can be found on
our website.
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