GRIM: Task-Oriented Grasping with Conditioning on Generative Examples
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911269368365056 |
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| author | Shailesh Raj, Alok Kumar, Nayan Shukla, Priya Melnik, Andrew Beetz, Michael Nandi, Gora Chand |
| author_facet | Shailesh Raj, Alok Kumar, Nayan Shukla, Priya Melnik, Andrew Beetz, Michael Nandi, Gora Chand |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15607 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GRIM: Task-Oriented Grasping with Conditioning on Generative Examples Shailesh Raj, Alok Kumar, Nayan Shukla, Priya Melnik, Andrew Beetz, Michael Nandi, Gora Chand Robotics 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 |
| title | GRIM: Task-Oriented Grasping with Conditioning on Generative Examples |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.15607 |