Scooping items with tools such as spoons and ladles is common in daily life, ranging from assistive feeding to retrieving items from environmental disaster sites. However, developing a general and autonomous robotic scooping policy is challenging since it requires reasoning about complex tool-object interactions. Furthermore, scooping often involves manipulating deformable objects, such as granular media or liquids, which is challenging due to their infinite-dimensional configuration spaces and complex dynamics. We propose a method, SCOOP'D, which uses simulation from OmniGibson (built on NVIDIA Omniverse) to collect scooping demonstrations using algorithmic procedures that rely on privileged state information. Then, we use generative policies via diffusion to imitate demonstrations from observational input. We directly apply the learned policy in diverse real-world scenarios, testing its performance on various item quantities, item characteristics, and container types. In zero-shot deployment, our method demonstrates promising results across 465 trials in diverse scenarios, including objects of different difficulty levels that we categorize as "Level 1" and "Level 2." SCOOP'D outperforms all baselines and ablations, suggesting that this is a promising approach to acquiring robotic scooping skills. Project page is at https://scoopdiff.github.io/.

翻译：使用勺子、长柄勺等工具进行舀取操作在日常生活中十分常见，其应用范围从辅助喂食到从环境灾害现场回收物品。然而，开发一种通用且自主的机器人舀取策略具有挑战性，因为它需要对复杂的工具-物体交互进行推理。此外，舀取通常涉及操纵可变形物体，例如颗粒介质或液体，由于其无限维的构型空间和复杂的动力学特性，这尤为困难。我们提出了一种名为SCOOP'D的方法，该方法利用OmniGibson（基于NVIDIA Omniverse构建）的仿真环境，通过依赖特权状态信息的算法程序来收集舀取演示数据。随后，我们使用基于扩散模型的生成策略，从观测输入中模仿这些演示。我们将学习到的策略直接应用于多样化的现实场景中，测试了其在不同物品数量、物品特性和容器类型下的性能。在零样本部署中，我们的方法在465次涵盖不同场景的试验中展现了良好的结果，这些场景包括我们归类为"一级"和"二级"不同难度等级的物体。SCOOP'D在所有基线方法和消融实验中均表现更优，表明这是获取机器人舀取技能的一种有前景的途径。项目页面位于 https://scoopdiff.github.io/。