{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:U6OYQAJTWBZUXYFNZAL6TT2CZQ","short_pith_number":"pith:U6OYQAJT","schema_version":"1.0","canonical_sha256":"a79d880133b0734be0adc817e9cf42cc13f8ae7498571e5cc4f794d2f39818c4","source":{"kind":"arxiv","id":"2607.16312","version":1},"attestation_state":"computed","paper":{"title":"xperception -- Making Robotic Grasping Easier","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Alice Fasoli, Andrea Caraffa, Fabio Poiesi, Matteo Bortolon","submitted_at":"2026-07-14T18:38:39Z","abstract_excerpt":"The transition toward high-mix low-volume manufacturing demands flexibility in robotic manipulation. However, conventional vision systems remain a bottleneck, requiring extensive data collection and model retraining whenever a new object is introduced to the production line. To overcome this rigidity, we present xperception, a zero-shot 6D pose estimation technology that eliminates the need for object-specific fine-tuning and laborious data annotation. By directly utilizing typical CAD models and integrating the rich semantic features of foundation models (e.g. DINOv2, GeDi), xperception achie"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.16312","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-14T18:38:39Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"b59361e39b906f619bd3e5e377c23b08c36c58df42636e609fe81aa6c03844ac","abstract_canon_sha256":"f7ead297b33f7923b3383c7eb64ee9a685253bbc56923ecdf04818026d7b30cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T00:20:11.741927Z","signature_b64":"gNlRFrrYxTKT10IrS5nsQD187BOcsgUEGIPGmoCoYnsFXRaMF1am1exlS6JhW81czXVKCAaNPB/dM+pyiLmbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a79d880133b0734be0adc817e9cf42cc13f8ae7498571e5cc4f794d2f39818c4","last_reissued_at":"2026-07-21T00:20:11.740926Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T00:20:11.740926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"xperception -- Making Robotic Grasping Easier","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Alice Fasoli, Andrea Caraffa, Fabio Poiesi, Matteo Bortolon","submitted_at":"2026-07-14T18:38:39Z","abstract_excerpt":"The transition toward high-mix low-volume manufacturing demands flexibility in robotic manipulation. However, conventional vision systems remain a bottleneck, requiring extensive data collection and model retraining whenever a new object is introduced to the production line. To overcome this rigidity, we present xperception, a zero-shot 6D pose estimation technology that eliminates the need for object-specific fine-tuning and laborious data annotation. By directly utilizing typical CAD models and integrating the rich semantic features of foundation models (e.g. DINOv2, GeDi), xperception achie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.16312","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.16312/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.16312","created_at":"2026-07-21T00:20:11.741427+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.16312v1","created_at":"2026-07-21T00:20:11.741427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.16312","created_at":"2026-07-21T00:20:11.741427+00:00"},{"alias_kind":"pith_short_12","alias_value":"U6OYQAJTWBZU","created_at":"2026-07-21T00:20:11.741427+00:00"},{"alias_kind":"pith_short_16","alias_value":"U6OYQAJTWBZUXYFN","created_at":"2026-07-21T00:20:11.741427+00:00"},{"alias_kind":"pith_short_8","alias_value":"U6OYQAJT","created_at":"2026-07-21T00:20:11.741427+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ","json":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ.json","graph_json":"https://pith.science/api/pith-number/U6OYQAJTWBZUXYFNZAL6TT2CZQ/graph.json","events_json":"https://pith.science/api/pith-number/U6OYQAJTWBZUXYFNZAL6TT2CZQ/events.json","paper":"https://pith.science/paper/U6OYQAJT"},"agent_actions":{"view_html":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ","download_json":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ.json","view_paper":"https://pith.science/paper/U6OYQAJT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.16312&json=true","fetch_graph":"https://pith.science/api/pith-number/U6OYQAJTWBZUXYFNZAL6TT2CZQ/graph.json","fetch_events":"https://pith.science/api/pith-number/U6OYQAJTWBZUXYFNZAL6TT2CZQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ/action/storage_attestation","attest_author":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ/action/author_attestation","sign_citation":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ/action/citation_signature","submit_replication":"https://pith.science/pith/U6OYQAJTWBZUXYFNZAL6TT2CZQ/action/replication_record"}},"created_at":"2026-07-21T00:20:11.741427+00:00","updated_at":"2026-07-21T00:20:11.741427+00:00"}