{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T08:04:40Z","timestamp":1773821080689,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"22","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Task-Oriented Grasping (TOG) presents a significant challenge, requiring a nuanced understanding of task semantics, object affordances, and the functional constraints dictating how an object should be grasped for a specific task. To address these challenges, we introduce GRIM (Grasp Re-alignment via Iterative Matching), a novel training-free framework for task-oriented grasping. Initially, a coarse alignment strategy is developed using a combination of geometric cues and principal component analysis (PCA)-reduced DINO features for similarity scoring. Subsequently, the full grasp pose associated with the retrieved memory instance is transferred to the aligned scene object and further refined against a set of task-agnostic, geometrically stable grasps generated for the scene object, prioritizing task compatibility. In contrast to existing learning-based methods, GRIM demonstrates strong generalization capabilities, achieving robust performance with only a small number of conditioning examples.<\/jats:p>","DOI":"10.1609\/aaai.v40i22.38873","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:04:42Z","timestamp":1773817482000},"page":"18118-18125","source":"Crossref","is-referenced-by-count":0,"title":["GRIM: Task-Oriented Grasping with Conditioning on Generative Examples"],"prefix":"10.1609","volume":"40","author":[{"family":"Shailesh","sequence":"first","affiliation":[]},{"given":"Alok","family":"Raj","sequence":"additional","affiliation":[]},{"given":"Nayan","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Priya","family":"Shukla","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Melnik","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Beetz","sequence":"additional","affiliation":[]},{"given":"Gora Chand","family":"Nandi","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38873\/42835","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38873\/42835","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:04:42Z","timestamp":1773817482000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38873"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i22.38873","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}