Learning Bimanual Scooping Policies for Food Acquisition
Learning Bimanual Scooping Policies for Food Acquisition
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DOI：
10.48550/arxiv.2211.14652
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
J. Grannen;Yilin Wu;Suneel Belkhale;Dorsa Sadigh
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文献类型：
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作者：
J. Grannen;Yilin Wu;Suneel Belkhale;Dorsa Sadigh
机器人喂食系统必须能够获得各种食物。先前的咬合采集工作考虑单臂勺子舀或叉串，这并不能推广到具有复杂几何形状和变形性的食物。例如，当获得一组豌豆时，串可以平滑豌豆，而没有障碍物的舀可以导致追逐盘子上的豌豆。为了获得具有如此不同性质的食物，我们提出在使用第二臂舀取期间稳定食物，例如，通过将豌豆推靠在具有平坦表面的勺子上以防止分散。增加的稳定臂可能会带来新的挑战。重要的是，这个手臂应该稳定食物场景而不干扰采集运动，这对于豆腐等易破碎的高风险食物尤其困难。这些高风险的食物可能会在舀取过程中在推料器和勺子之间破裂，这可能导致食物垃圾从勺子中掉出来。我们提出了一个一般的双手舀原语和自适应稳定策略，使成功收购的一组不同的食物几何形状和物理特性。我们的方法，CARBS：Coordinated Acquisition with Reactive Bimanual Scooping通过识别高风险食物并使用闭环视觉反馈稳健地舀取它们来学习稳定而不妨碍任务进度。我们发现CARBS能够概括食物的形状，大小和变形性，并且还能够同时操纵多种食物。CARBS在舀取刚性食物方面的成功率为87.0%，比单臂基线成功率高25.8%，与分析基线相比，食物破损率降低了16.2%。视频可以在https://sites.google.com/view/bimanualscoop-corl22/home上找到。
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 .