LAVA：基于长时程视觉动作的食物获取
LAVA: Long-horizon Visual Action based Food Acquisition
AI总结：
针对现有机器人辅助喂食对半固体和可变形食物操作不足的问题，提出LAVA分层策略，实现长时程食物获取，在46碗试验中成功率89±4%，优于基线。
摘要：
机器人辅助喂食（RAF）解决了行动不便个体在自主进食方面的基本需求。RAF的目标是使用机器人手臂从餐桌上获取食物并将其传递给个体。现有的RAF方法主要集中于固体食物，在半固体和可变形食物的操作策略方面存在空白。本研究引入了基于长时程视觉动作（LAVA）的液体、半固体和可变形食物获取方法。长时程指的是通过依次从碗中获取食物来实现“清空碗”的目标。LAVA采用分层策略来完成长时程食物获取任务。该框架利用高层策略通过ScoopNet来确定基本动作。在中层，LAVA使用视觉来寻找基本动作的参数。为了在现实世界中执行序列计划，LAVA将动作执行委托给低层策略，该策略使用从中层策略接收的参数和行为克隆来确保精确的轨迹执行。我们在涉及颗粒状、液体、半固体和可变形食物类型以及水果块和汤获取的复杂现实世界获取试验中验证了我们的方法。在46个碗的试验中，LAVA比基线方法更高效地获取食物，成功率为89±4%，并且能够泛化到现实中的盘子变化，如不同的位置、种类和碗中食物的量。代码、数据集、视频和补充材料可在我们的网站上找到。
英文摘要：
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% and generalizes across realistic 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
