TOSC：面向任务的形状补全用于从部分点云生成开放世界灵巧抓取
TOSC: Task-Oriented Shape Completion for Open-World Dexterous Grasp Generation from Partial Point Clouds
AI总结：
本文提出面向任务的形状补全方法，通过生成任务导向的接触区域完成方案，提升开放世界中灵巧抓取的性能，改进抓取位移和 Chamfer 距离。
摘要：
面向任务的灵巧抓取在开放世界对象的机器人操作中仍具挑战性，特别是在严重部分观察下，显著缺失数据会破坏通用形状补全。本文为克服这一限制，研究了面向任务的形状补全，该任务聚焦于补全潜在接触区域而非整个形状。我们主张形状补全用于抓取应明确由下游操作任务引导。为此，首先通过利用多个预训练基础模型的零样本能力生成多个任务导向的形状补全候选方案。然后提出一个 3D 区分自编码器，用于评估每个生成候选方案的合理性，并从全局视角优化最合理的方案。开发了一个名为 FlowGrasp 的条件流匹配模型，用于从优化后的形状生成任务导向的灵巧抓取。我们的方法在任务导向的灵巧抓取和任务导向的形状补全上实现了最先进的性能，将 Grasp Displacement 和 Chamfer Distance 分别提高了 16.17% 和 55.26%。特别是，它在严重缺失数据的抓取对象上表现出色。此外，它在处理开集类别和任务时也表现出良好的通用性。
英文摘要：
Task-oriented dexterous grasping remains challenging in robotic manipulations of open-world objects under severe partial observation, where significant missing data invalidates generic shape completion. In this paper, to overcome this limitation, we study Task-Oriented Shape Completion, a new task that focuses on completing the potential contact regions rather than the entire shape. We argue that shape completion for grasping should be explicitly guided by the downstream manipulation task. To achieve this, we first generate multiple task-oriented shape completion candidates by leveraging the zero-shot capabilities of object functional understanding from several pre-trained foundation models. A 3D discriminative autoencoder is then proposed to evaluate the plausibility of each generated candidate and optimize the most plausible one from a global perspective. A conditional flow-matching model named FlowGrasp is developed to generate task-oriented dexterous grasps from the optimized shape. Our method achieves state-of-the-art performance in task-oriented dexterous grasping and task-oriented shape completion, improving the Grasp Displacement and the Chamfer Distance over the state-of-the-art by 16.17\% and 55.26%, respectively. In particular, it shows good capabilities in grasping objects with severe missing data. It also demonstrates good generality in handling open-set categories and tasks.
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。
