{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GRKOU2FDDY7KB3YHAANYSD63CS","short_pith_number":"pith:GRKOU2FD","canonical_record":{"source":{"id":"2508.05153","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-08-07T08:37:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5ffbed46c4d371a255ba967e1a0e07f87a393a9292ee5429aa75487db22645f4","abstract_canon_sha256":"9dbca35915c0a469d47c61ed136fa3183a416cd41729c4d625e76370284a4251"},"schema_version":"1.0"},"canonical_sha256":"3454ea68a31e3ea0ef07001b890fdb1491e2d9ee404af276d914ceaa1bbfaa93","source":{"kind":"arxiv","id":"2508.05153","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.05153","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"arxiv_version","alias_value":"2508.05153v2","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.05153","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"pith_short_12","alias_value":"GRKOU2FDDY7K","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"pith_short_16","alias_value":"GRKOU2FDDY7KB3YH","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"pith_short_8","alias_value":"GRKOU2FD","created_at":"2026-06-09T01:05:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GRKOU2FDDY7KB3YHAANYSD63CS","target":"record","payload":{"canonical_record":{"source":{"id":"2508.05153","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-08-07T08:37:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5ffbed46c4d371a255ba967e1a0e07f87a393a9292ee5429aa75487db22645f4","abstract_canon_sha256":"9dbca35915c0a469d47c61ed136fa3183a416cd41729c4d625e76370284a4251"},"schema_version":"1.0"},"canonical_sha256":"3454ea68a31e3ea0ef07001b890fdb1491e2d9ee404af276d914ceaa1bbfaa93","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:08.595796Z","signature_b64":"UCDDpuqNJMctGdLkFxLT+8YfU3me3bpmeujMITM/gw8+Nbg7CT8107U55gXoX4gFFygPT/IaXFxnZq3AW+7QAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3454ea68a31e3ea0ef07001b890fdb1491e2d9ee404af276d914ceaa1bbfaa93","last_reissued_at":"2026-06-09T01:05:08.595273Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:08.595273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.05153","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T01:05:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BoTi20FZtr9JuFxdjatcGmLoZryZ7SsqyXqfi83GerflbfTrLXwnapnMcVENk8VdBIAviw4HeUnm/SbQ7Lz1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T14:26:16.697785Z"},"content_sha256":"1c9d009aa6183a9c06ffb05377bffd95d70c123669cda5773035a06a14638143","schema_version":"1.0","event_id":"sha256:1c9d009aa6183a9c06ffb05377bffd95d70c123669cda5773035a06a14638143"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GRKOU2FDDY7KB3YHAANYSD63CS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FCBV-Net: Category-Level Robotic Garment Smoothing via Feature-Conditioned Bimanual Value Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Conditioning bimanual action values on frozen 3D geometric features lets robots smooth unseen garments with far smaller performance loss than baselines.","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Jing Qiu, Mohammed Daba","submitted_at":"2025-08-07T08:37:45Z","abstract_excerpt":"Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due to high dimensionality, complex dynamics, and intra-category variations. Current approaches often struggle, either overfitting with concurrently learned visual features for a specific instance or, despite Category-level perceptual generalization, failing to predict the value of synergistic bimanual actions. We propose the Feature-Conditioned bimanual Value Network (FCBV-Net), operating on 3D point clouds to specifically enhance category-level policy generalization for ga"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"the decoupling of geometric understanding from bimanual action value learning enables better category-level generalization","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That pre-trained and frozen dense geometric features extracted from 3D point clouds remain sufficiently informative and robust across intra-category variations in the CLOTH3D dataset without any task-specific fine-tuning or adaptation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"FCBV-Net achieves superior category-level generalization for bimanual garment smoothing by conditioning value prediction on static pre-trained dense geometric features from point clouds, showing only 11.5% efficiency drop on unseen garments versus much larger drops in baselines.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Conditioning bimanual action values on frozen 3D geometric features lets robots smooth unseen garments with far smaller performance loss than baselines.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"580b48da755ffd9868b295002f46a5b1438f43f1cb9ada26b62770067be1470a"},"source":{"id":"2508.05153","kind":"arxiv","version":2},"verdict":{"id":"a401bd7d-83ce-4f72-955d-25dc0f4bd198","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T01:12:13.308879Z","strongest_claim":"the decoupling of geometric understanding from bimanual action value learning enables better category-level generalization","one_line_summary":"FCBV-Net achieves superior category-level generalization for bimanual garment smoothing by conditioning value prediction on static pre-trained dense geometric features from point clouds, showing only 11.5% efficiency drop on unseen garments versus much larger drops in baselines.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That pre-trained and frozen dense geometric features extracted from 3D point clouds remain sufficiently informative and robust across intra-category variations in the CLOTH3D dataset without any task-specific fine-tuning or adaptation.","pith_extraction_headline":"Conditioning bimanual action values on frozen 3D geometric features lets robots smooth unseen garments with far smaller performance loss than baselines."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.05153/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":27,"sample":[{"doi":"","year":2014,"title":"Ide ntifying the potential for robotics to assist older adults in differe nt living environments","work_id":"db79ed13-822e-4656-a2d3-7a2283fbbdb3","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2008,"title":"Managing activity difficulties at ho me: A survey of medicare beneficiaries","work_id":"4d3f5fc8-061c-470c-8165-ddc3977443fe","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Challenges and outlook in robotic manipula tion of deformable objects","work_id":"9fb447fa-4a8f-47e8-ab39-80188cc8d38c","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Unigarmentman ip: A unified framework for category-level garment manipulatio n via dense visual correspondence","work_id":"ce43107d-4c31-4e91-b754-667d13f1a000","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Speedfolding: Learning efficient bimanual folding of garm ents","work_id":"9b226935-fd30-4c0b-84a9-4992c2a7ae68","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":27,"snapshot_sha256":"9c774b658d248ce63a1a63b35474ecdc9472c8d160a37c8013cc56dc68616dee","internal_anchors":1},"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"},"verdict_id":"a401bd7d-83ce-4f72-955d-25dc0f4bd198"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T01:05:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"60xA1/t1nLc0tlkaM7Eyt2P2byVLmLui/ykaZxZYFxxqT3lLrMnudIp6PHxSDoooYEqwjPCR1fdcVYZNX7qHCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T14:26:16.698475Z"},"content_sha256":"53fc180614db8c2f3c6c16f4de0d048aab0672bc52e9794e2f0ad118e45c7250","schema_version":"1.0","event_id":"sha256:53fc180614db8c2f3c6c16f4de0d048aab0672bc52e9794e2f0ad118e45c7250"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GRKOU2FDDY7KB3YHAANYSD63CS/bundle.json","state_url":"https://pith.science/pith/GRKOU2FDDY7KB3YHAANYSD63CS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GRKOU2FDDY7KB3YHAANYSD63CS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-11T14:26:16Z","links":{"resolver":"https://pith.science/pith/GRKOU2FDDY7KB3YHAANYSD63CS","bundle":"https://pith.science/pith/GRKOU2FDDY7KB3YHAANYSD63CS/bundle.json","state":"https://pith.science/pith/GRKOU2FDDY7KB3YHAANYSD63CS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GRKOU2FDDY7KB3YHAANYSD63CS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GRKOU2FDDY7KB3YHAANYSD63CS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9dbca35915c0a469d47c61ed136fa3183a416cd41729c4d625e76370284a4251","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-08-07T08:37:45Z","title_canon_sha256":"5ffbed46c4d371a255ba967e1a0e07f87a393a9292ee5429aa75487db22645f4"},"schema_version":"1.0","source":{"id":"2508.05153","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.05153","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"arxiv_version","alias_value":"2508.05153v2","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.05153","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"pith_short_12","alias_value":"GRKOU2FDDY7K","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"pith_short_16","alias_value":"GRKOU2FDDY7KB3YH","created_at":"2026-06-09T01:05:08Z"},{"alias_kind":"pith_short_8","alias_value":"GRKOU2FD","created_at":"2026-06-09T01:05:08Z"}],"graph_snapshots":[{"event_id":"sha256:53fc180614db8c2f3c6c16f4de0d048aab0672bc52e9794e2f0ad118e45c7250","target":"graph","created_at":"2026-06-09T01:05:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"the decoupling of geometric understanding from bimanual action value learning enables better category-level generalization"},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That pre-trained and frozen dense geometric features extracted from 3D point clouds remain sufficiently informative and robust across intra-category variations in the CLOTH3D dataset without any task-specific fine-tuning or adaptation."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"FCBV-Net achieves superior category-level generalization for bimanual garment smoothing by conditioning value prediction on static pre-trained dense geometric features from point clouds, showing only 11.5% efficiency drop on unseen garments versus much larger drops in baselines."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Conditioning bimanual action values on frozen 3D geometric features lets robots smooth unseen garments with far smaller performance loss than baselines."}],"snapshot_sha256":"580b48da755ffd9868b295002f46a5b1438f43f1cb9ada26b62770067be1470a"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.05153/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due to high dimensionality, complex dynamics, and intra-category variations. Current approaches often struggle, either overfitting with concurrently learned visual features for a specific instance or, despite Category-level perceptual generalization, failing to predict the value of synergistic bimanual actions. We propose the Feature-Conditioned bimanual Value Network (FCBV-Net), operating on 3D point clouds to specifically enhance category-level policy generalization for ga","authors_text":"Jing Qiu, Mohammed Daba","cross_cats":["cs.AI"],"headline":"Conditioning bimanual action values on frozen 3D geometric features lets robots smooth unseen garments with far smaller performance loss than baselines.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-08-07T08:37:45Z","title":"FCBV-Net: Category-Level Robotic Garment Smoothing via Feature-Conditioned Bimanual Value Prediction"},"references":{"count":27,"internal_anchors":1,"resolved_work":27,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Ide ntifying the potential for robotics to assist older adults in differe nt living environments","work_id":"db79ed13-822e-4656-a2d3-7a2283fbbdb3","year":2014},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Managing activity difficulties at ho me: A survey of medicare beneficiaries","work_id":"4d3f5fc8-061c-470c-8165-ddc3977443fe","year":2008},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Challenges and outlook in robotic manipula tion of deformable objects","work_id":"9fb447fa-4a8f-47e8-ab39-80188cc8d38c","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Unigarmentman ip: A unified framework for category-level garment manipulatio n via dense visual correspondence","work_id":"ce43107d-4c31-4e91-b754-667d13f1a000","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Speedfolding: Learning efficient bimanual folding of garm ents","work_id":"9b226935-fd30-4c0b-84a9-4992c2a7ae68","year":2022}],"snapshot_sha256":"9c774b658d248ce63a1a63b35474ecdc9472c8d160a37c8013cc56dc68616dee"},"source":{"id":"2508.05153","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-19T01:12:13.308879Z","id":"a401bd7d-83ce-4f72-955d-25dc0f4bd198","model_set":{"reader":"grok-4.3"},"one_line_summary":"FCBV-Net achieves superior category-level generalization for bimanual garment smoothing by conditioning value prediction on static pre-trained dense geometric features from point clouds, showing only 11.5% efficiency drop on unseen garments versus much larger drops in baselines.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Conditioning bimanual action values on frozen 3D geometric features lets robots smooth unseen garments with far smaller performance loss than baselines.","strongest_claim":"the decoupling of geometric understanding from bimanual action value learning enables better category-level generalization","weakest_assumption":"That pre-trained and frozen dense geometric features extracted from 3D point clouds remain sufficiently informative and robust across intra-category variations in the CLOTH3D dataset without any task-specific fine-tuning or adaptation."}},"verdict_id":"a401bd7d-83ce-4f72-955d-25dc0f4bd198"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1c9d009aa6183a9c06ffb05377bffd95d70c123669cda5773035a06a14638143","target":"record","created_at":"2026-06-09T01:05:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9dbca35915c0a469d47c61ed136fa3183a416cd41729c4d625e76370284a4251","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-08-07T08:37:45Z","title_canon_sha256":"5ffbed46c4d371a255ba967e1a0e07f87a393a9292ee5429aa75487db22645f4"},"schema_version":"1.0","source":{"id":"2508.05153","kind":"arxiv","version":2}},"canonical_sha256":"3454ea68a31e3ea0ef07001b890fdb1491e2d9ee404af276d914ceaa1bbfaa93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3454ea68a31e3ea0ef07001b890fdb1491e2d9ee404af276d914ceaa1bbfaa93","first_computed_at":"2026-06-09T01:05:08.595273Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:08.595273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UCDDpuqNJMctGdLkFxLT+8YfU3me3bpmeujMITM/gw8+Nbg7CT8107U55gXoX4gFFygPT/IaXFxnZq3AW+7QAg==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:08.595796Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.05153","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c9d009aa6183a9c06ffb05377bffd95d70c123669cda5773035a06a14638143","sha256:53fc180614db8c2f3c6c16f4de0d048aab0672bc52e9794e2f0ad118e45c7250"],"state_sha256":"0fca043a0f15857427ebf29d0c8336717191da09b133321ed61a814030c5d228"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l5xljiH3ZVacL0EC/PvOksKnYlVaGG80DiOJ+7sqi4ti1FabUQnlbW5FNKLlSXEYxW0Ob40YDniDoJld8+Z0Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T14:26:16.701155Z","bundle_sha256":"7473c36d7422a9b7026e80b2b41e73d7ebed51265fa4daa4d2bf079ecdc72f8a"}}