{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QPKVORSBDYIV5OKJOB7ALACESK","short_pith_number":"pith:QPKVORSB","schema_version":"1.0","canonical_sha256":"83d55746411e115eb949707e058044929622af4db771de8aa10d3c818910c587","source":{"kind":"arxiv","id":"2308.16891","version":3},"attestation_state":"computed","paper":{"title":"GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Annabella Macaluso, Ge Yan, Jianglong Ye, Li Erran Li, Nicklas Hansen, Xiaolong Wang, Yanjie Ze, Yueh-Hua Wu, Yuying Ge","submitted_at":"2023-08-31T17:52:10Z","abstract_excerpt":"It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot needs to have a comprehensive understanding of the 3D structure and semantics of the scene. In this work, we present $\\textbf{GNFactor}$, a visual behavior cloning agent for multi-task robotic manipulation with $\\textbf{G}$eneralizable $\\textbf{N}$eural feature $\\textbf{F}$ields. GNFactor jointly optimizes a generalizable neural field (GNF) as a reconstruction module and a Perceiver Transfor"},"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":"2308.16891","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-08-31T17:52:10Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"792000fc38b10f2a4afdb8d22083fe8f4bc53efd389283ebcc8a83e0c9f7ca71","abstract_canon_sha256":"db6a4ca5efb2f0bc9aae7ed7eb655220f78c468c2f8301d7d1c88f6792b70e18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:49:25.485945Z","signature_b64":"7WYkaA+V8NWAzRQ4SQIpwS47GwSPVRI/ooVXp2r2Z5CB5/rv29oUqY+GAWPOoBAiwx8oK6ODfhOTR+E9A585DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83d55746411e115eb949707e058044929622af4db771de8aa10d3c818910c587","last_reissued_at":"2026-07-05T08:49:25.485588Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:49:25.485588Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Annabella Macaluso, Ge Yan, Jianglong Ye, Li Erran Li, Nicklas Hansen, Xiaolong Wang, Yanjie Ze, Yueh-Hua Wu, Yuying Ge","submitted_at":"2023-08-31T17:52:10Z","abstract_excerpt":"It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot needs to have a comprehensive understanding of the 3D structure and semantics of the scene. In this work, we present $\\textbf{GNFactor}$, a visual behavior cloning agent for multi-task robotic manipulation with $\\textbf{G}$eneralizable $\\textbf{N}$eural feature $\\textbf{F}$ields. GNFactor jointly optimizes a generalizable neural field (GNF) as a reconstruction module and a Perceiver Transfor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.16891","kind":"arxiv","version":3},"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/2308.16891/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":"2308.16891","created_at":"2026-07-05T08:49:25.485644+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.16891v3","created_at":"2026-07-05T08:49:25.485644+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.16891","created_at":"2026-07-05T08:49:25.485644+00:00"},{"alias_kind":"pith_short_12","alias_value":"QPKVORSBDYIV","created_at":"2026-07-05T08:49:25.485644+00:00"},{"alias_kind":"pith_short_16","alias_value":"QPKVORSBDYIV5OKJ","created_at":"2026-07-05T08:49:25.485644+00:00"},{"alias_kind":"pith_short_8","alias_value":"QPKVORSB","created_at":"2026-07-05T08:49:25.485644+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2402.10885","citing_title":"3D Diffuser Actor: Policy Diffusion with 3D Scene Representations","ref_index":69,"is_internal_anchor":false},{"citing_arxiv_id":"2603.05117","citing_title":"SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK","json":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK.json","graph_json":"https://pith.science/api/pith-number/QPKVORSBDYIV5OKJOB7ALACESK/graph.json","events_json":"https://pith.science/api/pith-number/QPKVORSBDYIV5OKJOB7ALACESK/events.json","paper":"https://pith.science/paper/QPKVORSB"},"agent_actions":{"view_html":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK","download_json":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK.json","view_paper":"https://pith.science/paper/QPKVORSB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.16891&json=true","fetch_graph":"https://pith.science/api/pith-number/QPKVORSBDYIV5OKJOB7ALACESK/graph.json","fetch_events":"https://pith.science/api/pith-number/QPKVORSBDYIV5OKJOB7ALACESK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK/action/storage_attestation","attest_author":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK/action/author_attestation","sign_citation":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK/action/citation_signature","submit_replication":"https://pith.science/pith/QPKVORSBDYIV5OKJOB7ALACESK/action/replication_record"}},"created_at":"2026-07-05T08:49:25.485644+00:00","updated_at":"2026-07-05T08:49:25.485644+00:00"}