{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:7AS2MT5Q4LFEW2PAA2FIMI5YWG","short_pith_number":"pith:7AS2MT5Q","canonical_record":{"source":{"id":"1803.11469","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-30T13:56:19Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"c1f52ed14aff659c712df5d22f6c1b57842ffd73835bf26ec82ebaf10ce49186","abstract_canon_sha256":"4ad2e900aafc8d89e4c8c2dba8ecba95e464a3fb657e424c0f5db71190e400bd"},"schema_version":"1.0"},"canonical_sha256":"f825a64fb0e2ca4b69e0068a8623b8b1ae8fabffd3069f2260f4b112f5d8f12b","source":{"kind":"arxiv","id":"1803.11469","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.11469","created_at":"2026-05-18T00:04:34Z"},{"alias_kind":"arxiv_version","alias_value":"1803.11469v2","created_at":"2026-05-18T00:04:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.11469","created_at":"2026-05-18T00:04:34Z"},{"alias_kind":"pith_short_12","alias_value":"7AS2MT5Q4LFE","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7AS2MT5Q4LFEW2PA","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7AS2MT5Q","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:7AS2MT5Q4LFEW2PAA2FIMI5YWG","target":"record","payload":{"canonical_record":{"source":{"id":"1803.11469","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-30T13:56:19Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"c1f52ed14aff659c712df5d22f6c1b57842ffd73835bf26ec82ebaf10ce49186","abstract_canon_sha256":"4ad2e900aafc8d89e4c8c2dba8ecba95e464a3fb657e424c0f5db71190e400bd"},"schema_version":"1.0"},"canonical_sha256":"f825a64fb0e2ca4b69e0068a8623b8b1ae8fabffd3069f2260f4b112f5d8f12b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:34.097345Z","signature_b64":"Ks5tqxH5DFqUq0Peu01bbkOdeZX+rzB0tTn1IJDxARCIcQsCZkrULFATQ7SsNIYWQUfbwrHuZ+f1wqwBvInmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f825a64fb0e2ca4b69e0068a8623b8b1ae8fabffd3069f2260f4b112f5d8f12b","last_reissued_at":"2026-05-18T00:04:34.096646Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:34.096646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.11469","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":"2026-05-18T00:04:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tCJUQUnA4dY1Pp3By/ybwCXvogtbe0jT4+arIbNyNZJPumuiHUHhbCX4B1x99RNNbljxCTLAYGgH4VJIuytUBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T13:40:44.905759Z"},"content_sha256":"320dcbd6967cef434b0bfcdca34b4b4cbef5f8dc3dd1dc9b881c33789be1c649","schema_version":"1.0","event_id":"sha256:320dcbd6967cef434b0bfcdca34b4b4cbef5f8dc3dd1dc9b881c33789be1c649"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:7AS2MT5Q4LFEW2PAA2FIMI5YWG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Jacquard: A Large Scale Dataset for Robotic Grasp Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.RO","authors_text":"Amaury Depierre (imagine), Emmanuel Dellandr\\'ea (imagine), Liming Chen (imagine)","submitted_at":"2018-03-30T13:56:19Z","abstract_excerpt":"Grasping skill is a major ability that a wide number of real-life applications require for robotisation. State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks. However, such networks require huge amount of labeled data for training making this approach often impracticable in robotics. In this paper, we propose a method to generate a large scale synthetic dataset with ground truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D images"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.11469","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:04:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SkjzUDHZwwtzhN8WKtZBXqCwHC4MrWkzJl8vZFYL4URnqK4zVOnSrNQbfx383y0K6kwtoTWZVRDdTTD+Daj/DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T13:40:44.906114Z"},"content_sha256":"7002e94f72809644bdfc2160b27ea0a807fa64ecb5c40885979c2bf09f553489","schema_version":"1.0","event_id":"sha256:7002e94f72809644bdfc2160b27ea0a807fa64ecb5c40885979c2bf09f553489"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7AS2MT5Q4LFEW2PAA2FIMI5YWG/bundle.json","state_url":"https://pith.science/pith/7AS2MT5Q4LFEW2PAA2FIMI5YWG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7AS2MT5Q4LFEW2PAA2FIMI5YWG/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":"2026-06-28T13:40:44Z","links":{"resolver":"https://pith.science/pith/7AS2MT5Q4LFEW2PAA2FIMI5YWG","bundle":"https://pith.science/pith/7AS2MT5Q4LFEW2PAA2FIMI5YWG/bundle.json","state":"https://pith.science/pith/7AS2MT5Q4LFEW2PAA2FIMI5YWG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7AS2MT5Q4LFEW2PAA2FIMI5YWG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7AS2MT5Q4LFEW2PAA2FIMI5YWG","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":"4ad2e900aafc8d89e4c8c2dba8ecba95e464a3fb657e424c0f5db71190e400bd","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-30T13:56:19Z","title_canon_sha256":"c1f52ed14aff659c712df5d22f6c1b57842ffd73835bf26ec82ebaf10ce49186"},"schema_version":"1.0","source":{"id":"1803.11469","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.11469","created_at":"2026-05-18T00:04:34Z"},{"alias_kind":"arxiv_version","alias_value":"1803.11469v2","created_at":"2026-05-18T00:04:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.11469","created_at":"2026-05-18T00:04:34Z"},{"alias_kind":"pith_short_12","alias_value":"7AS2MT5Q4LFE","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7AS2MT5Q4LFEW2PA","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7AS2MT5Q","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:7002e94f72809644bdfc2160b27ea0a807fa64ecb5c40885979c2bf09f553489","target":"graph","created_at":"2026-05-18T00:04:34Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Grasping skill is a major ability that a wide number of real-life applications require for robotisation. State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks. However, such networks require huge amount of labeled data for training making this approach often impracticable in robotics. In this paper, we propose a method to generate a large scale synthetic dataset with ground truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D images","authors_text":"Amaury Depierre (imagine), Emmanuel Dellandr\\'ea (imagine), Liming Chen (imagine)","cross_cats":["cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-30T13:56:19Z","title":"Jacquard: A Large Scale Dataset for Robotic Grasp Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.11469","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:320dcbd6967cef434b0bfcdca34b4b4cbef5f8dc3dd1dc9b881c33789be1c649","target":"record","created_at":"2026-05-18T00:04:34Z","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":"4ad2e900aafc8d89e4c8c2dba8ecba95e464a3fb657e424c0f5db71190e400bd","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-03-30T13:56:19Z","title_canon_sha256":"c1f52ed14aff659c712df5d22f6c1b57842ffd73835bf26ec82ebaf10ce49186"},"schema_version":"1.0","source":{"id":"1803.11469","kind":"arxiv","version":2}},"canonical_sha256":"f825a64fb0e2ca4b69e0068a8623b8b1ae8fabffd3069f2260f4b112f5d8f12b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f825a64fb0e2ca4b69e0068a8623b8b1ae8fabffd3069f2260f4b112f5d8f12b","first_computed_at":"2026-05-18T00:04:34.096646Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:34.096646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ks5tqxH5DFqUq0Peu01bbkOdeZX+rzB0tTn1IJDxARCIcQsCZkrULFATQ7SsNIYWQUfbwrHuZ+f1wqwBvInmBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:34.097345Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.11469","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:320dcbd6967cef434b0bfcdca34b4b4cbef5f8dc3dd1dc9b881c33789be1c649","sha256:7002e94f72809644bdfc2160b27ea0a807fa64ecb5c40885979c2bf09f553489"],"state_sha256":"82cf8eebb3f245affc7db950145fc5704777e705a7f4b6385501fed023240545"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oRdHnOD+R1IWRHljFkBOza4fU5100MHBKCKIjkPx8l6aMsSajb1ceAma+mF1jSRnlwYZaTkim2PChUymTYOXCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T13:40:44.907992Z","bundle_sha256":"84995b592e95678e14590fbaf9fd37e506a307ce677703bd9b27e3318f4eda71"}}