arXiv cs.AI· Manav Kulshrestha, Apoorva Vashisth, Damon Conover, Aniket Bera·· 2 天前AI 评分24
TGrasp6D：用视觉语言先验与位姿优化实现零样本任务导向 6DoF 抓取
TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping
AI 导读
TGrasp6D 是一种基于视觉语言的方法，可在零样本条件下为给定任务和单张 RGB-D 图像生成任务导向的 6DoF 抓取。它先推理任务对应的语义可供性与物体几何生成任务先验，再通过 6DoF 位姿优化在避免碰撞的同时保持任务一致性。
正文 · 原文
Abstract:Task-Oriented Grasping (TOG) has emerged as an important skill for purposeful robotic manipulation. Given the task of using a knife to "slice bread," a robot must choose to grasp from the handle, whereas for "hand[ing] the knife over," it is preferable for the handle to remain unobstructed for safe handoff. Current approaches for TOG either focus on 2D planar grasping, remain limited by their reliance on task-agnostic grasp generators, or are constrained by reliance on finite, manually-annotated, task-specific grasp datasets that cannot cover all possible grasp-task pairs. To that end, we present TGrasp6D, a vision-language based approach for generating task-oriented 6DoF grasps in a zero-shot manner. Given a task and a single RGB-D image of an associated object, TGrasp6D reasons about the appropriate semantic affordance for the given task and object geometry to produce an initial task- informed prior. This generated prior is further refined via 6DoF pose optimization for collision avoidance while preserving task consistency. Our approach achieves state-of-the-art success rate of 94% on the TaskGrasp dataset, with the best baseline at 74%. We also deploy our approach on a Franka Research 3 robot to demonstrate zero-shot generalization to real world objects
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20901 [cs.RO] |
| (or arXiv:2610.20901v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20901 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Manav Kulshrestha [view email]
[v1]
Wed, 14 Oct 2026 20:31:36 UTC (3,573 KB)
来源：arXiv cs.AI · arxiv.org