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
@article{arxiv.2506.15607,
title = {GRIM: Task-Oriented Grasping with Conditioning on Generative Examples},
author = {Shailesh and Alok Raj and Nayan Kumar and Priya Shukla and Andrew Melnik and Michael Beetz and Gora Chand Nandi},
journal= {arXiv preprint arXiv:2506.15607},
year = {2025}
}
Comments
Accepted to AAAI-26 (Oral). Project website: https://grim-tog.github.io