{"paper":{"title":"AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"By matching semantic keypoints across 3D meshes, AffordGen generates varied manipulation trajectories that let trained policies succeed on objects never seen in the original data.","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Huazhe Xu, Jiawei Zhang, Kaizhe Hu, Yingqian Huang, Yuanchen Ju, Zhengrong Xue","submitted_at":"2026-04-12T10:56:31Z","abstract_excerpt":"Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity. Leveraging powerful 3D generative models and vision foundation models (VFMs), the proposed AffordGen framework overcomes this limitation by utilizing the semantic correspondence of meaningful keypoints across large-scale 3D meshes to generate new robot manipulation trajectories. This large-scale, affordance-aware dataset is then used to train a robust, closed-loop visuomotor policy, combining the semantic generaliza"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments in simulation and the real world show that policies trained with AffordGen achieve high success rates and enable zero-shot generalization to truly unseen objects, significantly improving data efficiency in robot learning.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That semantic correspondence of meaningful keypoints across large-scale 3D meshes can reliably generate new, valid, and useful robot manipulation trajectories that transfer to real-world closed-loop control.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AffordGen generates affordance-aware manipulation demonstrations from 3D mesh correspondences to train policies with zero-shot generalization to novel objects.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"By matching semantic keypoints across 3D meshes, AffordGen generates varied manipulation trajectories that let trained policies succeed on objects never seen in the original data.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"244379fc75300955c1e7f4d97636c05b4b25146de6f1524d27b12ed6997e133b"},"source":{"id":"2604.10579","kind":"arxiv","version":2},"verdict":{"id":"98d77b94-65b3-4d47-8cab-d8c43c8ebf52","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T16:17:27.534735Z","strongest_claim":"Experiments in simulation and the real world show that policies trained with AffordGen achieve high success rates and enable zero-shot generalization to truly unseen objects, significantly improving data efficiency in robot learning.","one_line_summary":"AffordGen generates affordance-aware manipulation demonstrations from 3D mesh correspondences to train policies with zero-shot generalization to novel objects.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That semantic correspondence of meaningful keypoints across large-scale 3D meshes can reliably generate new, valid, and useful robot manipulation trajectories that transfer to real-world closed-loop control.","pith_extraction_headline":"By matching semantic keypoints across 3D meshes, AffordGen generates varied manipulation trajectories that let trained policies succeed on objects never seen in the original data."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.10579/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}