用生成视频模型找适合任务的抓取姿势，无需训练就能通用
GRIM: Task-Oriented Grasping with Conditioning on Generative Examples
- 通过检索生成样例，迭代匹配最优抓取姿态
- 在标准数据集上达到当前最好性能，无需额外训练
- 适合想快速部署抓取系统的机器人研发者
任务导向抓取（TOG）要求机器人选择对特定任务功能合适的抓取方式，这需要理解任务语义、物体功能属性和约束。我们提出GRIM（通过迭代匹配实现抓取重对齐），一种无需训练的框架，利用视频生成模型（VGMs）与检索-对齐-迁移流程应对这些挑战。GRIM不仅能利用VGM，还能从网络图像、人类示范或生成模型中构建物体-任务样例记忆库。检索到的任务适配抓取姿势随后通过几何稳定候选抓取进行评估与优化，确保功能适用性和物理可行性。GRIM展现出强大泛化能力，在标准TOG基准测试中达到领先性能。项目网站：https://grim-tog.github.io
原文摘要 · Abstract (English)
Task-Oriented Grasping (TOG) requires robots to select grasps that are functionally appropriate for a specified task - a challenge that demands an understanding of task semantics, object affordances, and functional constraints. We present GRIM (Grasp Re-alignment via Iterative Matching), a training-free framework that addresses these challenges by leveraging Video Generation Models (VGMs) together with a retrieve-align-transfer pipeline. Beyond leveraging VGMs, GRIM can construct a memory of object-task exemplars sourced from web images, human demonstrations, or generative models. The retrieved task-oriented grasp is then transferred and refined by evaluating it against a set of geometrically stable candidate grasps to ensure both functional suitability and physical feasibility. GRIM demonstrates strong generalization and achieves state-of-the-art performance on standard TOG benchmarks. Project website: https://grim-tog.github.io
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