{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QI7MSSOWB2B6OR6UUZIQ6X2CFW","short_pith_number":"pith:QI7MSSOW","schema_version":"1.0","canonical_sha256":"823ec949d60e83e747d4a6510f5f422d96a1529e41b78027f7e0cbde917f3b1f","source":{"kind":"arxiv","id":"2505.04860","version":2},"attestation_state":"computed","paper":{"title":"D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Daniel Seita, Gaurav Sukhatme, I-Chun Arthur Liu, Jason Chen","submitted_at":"2025-05-08T00:03:04Z","abstract_excerpt":"Learning bimanual manipulation is challenging due to its high dimensionality and tight coordination required between two arms. Eye-in-hand imitation learning, which uses wrist-mounted cameras, simplifies perception by focusing on task-relevant views. However, collecting diverse demonstrations remains costly, motivating the need for scalable data augmentation. While prior work has explored visual augmentation in single-arm settings, extending these approaches to bimanual manipulation requires generating viewpoint-consistent observations across both arms and producing corresponding action labels"},"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":"2505.04860","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-08T00:03:04Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"b3180824e45b6c2b191f368c835090bf18fceaa9415c25c75b49ce9a31d69665","abstract_canon_sha256":"75055b51c85e4ac93a790e5e51e749ef5ea3eefbcf44c73616af9b299b3c037c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:46.733866Z","signature_b64":"pTuB643Dt6fSuGyAJiiZWcG0ocjeLhOmLd9Ar5p1ARz5YILGkms8I85aV4+0aMDiAQZDZE3pn1xriNXIqkq3CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"823ec949d60e83e747d4a6510f5f422d96a1529e41b78027f7e0cbde917f3b1f","last_reissued_at":"2026-07-05T11:54:46.733350Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:46.733350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Daniel Seita, Gaurav Sukhatme, I-Chun Arthur Liu, Jason Chen","submitted_at":"2025-05-08T00:03:04Z","abstract_excerpt":"Learning bimanual manipulation is challenging due to its high dimensionality and tight coordination required between two arms. Eye-in-hand imitation learning, which uses wrist-mounted cameras, simplifies perception by focusing on task-relevant views. However, collecting diverse demonstrations remains costly, motivating the need for scalable data augmentation. While prior work has explored visual augmentation in single-arm settings, extending these approaches to bimanual manipulation requires generating viewpoint-consistent observations across both arms and producing corresponding action labels"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04860","kind":"arxiv","version":2},"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/2505.04860/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":"2505.04860","created_at":"2026-07-05T11:54:46.733415+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.04860v2","created_at":"2026-07-05T11:54:46.733415+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04860","created_at":"2026-07-05T11:54:46.733415+00:00"},{"alias_kind":"pith_short_12","alias_value":"QI7MSSOWB2B6","created_at":"2026-07-05T11:54:46.733415+00:00"},{"alias_kind":"pith_short_16","alias_value":"QI7MSSOWB2B6OR6U","created_at":"2026-07-05T11:54:46.733415+00:00"},{"alias_kind":"pith_short_8","alias_value":"QI7MSSOW","created_at":"2026-07-05T11:54:46.733415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.21256","citing_title":"BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW","json":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW.json","graph_json":"https://pith.science/api/pith-number/QI7MSSOWB2B6OR6UUZIQ6X2CFW/graph.json","events_json":"https://pith.science/api/pith-number/QI7MSSOWB2B6OR6UUZIQ6X2CFW/events.json","paper":"https://pith.science/paper/QI7MSSOW"},"agent_actions":{"view_html":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW","download_json":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW.json","view_paper":"https://pith.science/paper/QI7MSSOW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.04860&json=true","fetch_graph":"https://pith.science/api/pith-number/QI7MSSOWB2B6OR6UUZIQ6X2CFW/graph.json","fetch_events":"https://pith.science/api/pith-number/QI7MSSOWB2B6OR6UUZIQ6X2CFW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW/action/storage_attestation","attest_author":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW/action/author_attestation","sign_citation":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW/action/citation_signature","submit_replication":"https://pith.science/pith/QI7MSSOWB2B6OR6UUZIQ6X2CFW/action/replication_record"}},"created_at":"2026-07-05T11:54:46.733415+00:00","updated_at":"2026-07-05T11:54:46.733415+00:00"}