用语言指导的扩散模型，让机械手学会复杂任务下的灵活抓取。
DexTOG: Learning Task-Oriented Dexterous Grasp with Language
- 基于扩散模型生成符合任务要求的抓取姿态
- 构建了包含80个物体的DexTOG-80K数据集
- 适合研究灵巧手抓取与语言引导机器人控制的学者
本研究提出一种新型语言引导的扩散学习框架DexTOG，旨在推进灵巧手的任务导向抓取（TOG）技术。与以往聚焦于双指夹爪的方法不同，该研究应对灵巧操作中的多重挑战：在特定任务约束下识别非唯一最优抓取姿态、支持多种有效抓法，并在高自由度配置空间中进行抓取规划。DexTOG包含基于扩散的抓取姿态生成模型DexDiffu及配套数据引擎。通过该框架，我们构建了新数据集DexTOG-80K，使用影子机器人手在5类共80个物体上完成多项任务，充分展现机械手的灵巧性与多任务能力。本研究不仅显著提升灵巧手任务导向抓取水平，还提供全面的数据集与仿真验证，为机器人操作研究设立新基准。
原文摘要 · Abstract (English)
This study introduces a novel language-guided diffusion-based learning framework, DexTOG, aimed at advancing the field of task-oriented grasping (TOG) with dexterous hands. Unlike existing methods that mainly focus on 2-finger grippers, this research addresses the complexities of dexterous manipulation, where the system must identify non-unique optimal grasp poses under specific task constraints, cater to multiple valid grasps, and search in a high degree-of-freedom configuration space in grasp planning. The proposed DexTOG includes a diffusion-based grasp pose generation model, DexDiffu, and a data engine to support the DexDiffu. By leveraging DexTOG, we also proposed a new dataset, DexTOG-80K, which was developed using a shadow robot hand to perform various tasks on 80 objects from 5 categories, showcasing the dexterity and multi-tasking capabilities of the robotic hand. This research not only presents a significant leap in dexterous TOG but also provides a comprehensive dataset and simulation validation, setting a new benchmark in robotic manipulation research.
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