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arXiv · 2506.15607

GRIM: Task-Oriented Grasping with Conditioning on Generative Examples

Shailesh·Alok Raj·Nayan Kumar·Priya Shukla·Andrew Melnik·Michael Beetz·Gora Chand Nandi
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Abstract

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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Shailesh, Alok Raj, Nayan Kumar, Priya Shukla, Andrew Melnik, Michael Beetz, Gora Chand Nandi. 2025-06-18. GRIM: Task-Oriented Grasping with Conditioning on Generative Examples. https://arxiv.org/abs/2506.15607

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