{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TAZ7BNV4M56L3PWY6EMQZO5UNT","short_pith_number":"pith:TAZ7BNV4","schema_version":"1.0","canonical_sha256":"9833f0b6bc677cbdbed8f1190cbbb46ce54f5a03553d72fd8fc74f15f371a065","source":{"kind":"arxiv","id":"1809.10790","version":1},"attestation_state":"computed","paper":{"title":"Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Balakumar Sundaralingam, Dieter Fox, Jonathan Tremblay, Stan Birchfield, Thang To, Yu Xiang","submitted_at":"2018-09-27T22:45:53Z","abstract_excerpt":"Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out of harm's way. One of the key challenges of synthetic data, to date, has been to bridge the so-called reality gap, so that networks trained on synthetic data operate correctly when exposed to real-world data. We explore the reality gap in the context of 6-DoF pose estimation of known objects from a single RGB image. We show that for this problem the reality gap can be successfully spanned by a simple combination of do"},"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":"1809.10790","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-09-27T22:45:53Z","cross_cats_sorted":[],"title_canon_sha256":"ac0ea31ff36e0ba8c33165417b127d44cbed8595cf2abe80cf55a42c851ae713","abstract_canon_sha256":"d2a2683593e3c11cc6c3ccb8d80f2ed23a97e387107a7480a91cf9142efeb1ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:35.090144Z","signature_b64":"V5DTaJUN25+n6x5ui8YzbsopFGOk+5p8bxVH1wS/EXKYJ0YOYp4GdGicPGfwQaEn8YFJwqhKQ/uOFPAV3mXnAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9833f0b6bc677cbdbed8f1190cbbb46ce54f5a03553d72fd8fc74f15f371a065","last_reissued_at":"2026-05-18T00:04:35.089681Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:35.089681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Balakumar Sundaralingam, Dieter Fox, Jonathan Tremblay, Stan Birchfield, Thang To, Yu Xiang","submitted_at":"2018-09-27T22:45:53Z","abstract_excerpt":"Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out of harm's way. One of the key challenges of synthetic data, to date, has been to bridge the so-called reality gap, so that networks trained on synthetic data operate correctly when exposed to real-world data. We explore the reality gap in the context of 6-DoF pose estimation of known objects from a single RGB image. We show that for this problem the reality gap can be successfully spanned by a simple combination of do"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.10790","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":""},"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":"1809.10790","created_at":"2026-05-18T00:04:35.089750+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.10790v1","created_at":"2026-05-18T00:04:35.089750+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.10790","created_at":"2026-05-18T00:04:35.089750+00:00"},{"alias_kind":"pith_short_12","alias_value":"TAZ7BNV4M56L","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"TAZ7BNV4M56L3PWY","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"TAZ7BNV4","created_at":"2026-05-18T12:32:53.628368+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2603.19538","citing_title":"MoCA3D: Monocular 3D Bounding Box Prediction in the Image Plane","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2507.00990","citing_title":"Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations","ref_index":111,"is_internal_anchor":true},{"citing_arxiv_id":"2409.01652","citing_title":"ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2604.02759","citing_title":"OMNI-PoseX: A Fast Vision Model for 6D Object Pose Estimation in Embodied Tasks","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT","json":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT.json","graph_json":"https://pith.science/api/pith-number/TAZ7BNV4M56L3PWY6EMQZO5UNT/graph.json","events_json":"https://pith.science/api/pith-number/TAZ7BNV4M56L3PWY6EMQZO5UNT/events.json","paper":"https://pith.science/paper/TAZ7BNV4"},"agent_actions":{"view_html":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT","download_json":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT.json","view_paper":"https://pith.science/paper/TAZ7BNV4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.10790&json=true","fetch_graph":"https://pith.science/api/pith-number/TAZ7BNV4M56L3PWY6EMQZO5UNT/graph.json","fetch_events":"https://pith.science/api/pith-number/TAZ7BNV4M56L3PWY6EMQZO5UNT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT/action/storage_attestation","attest_author":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT/action/author_attestation","sign_citation":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT/action/citation_signature","submit_replication":"https://pith.science/pith/TAZ7BNV4M56L3PWY6EMQZO5UNT/action/replication_record"}},"created_at":"2026-05-18T00:04:35.089750+00:00","updated_at":"2026-05-18T00:04:35.089750+00:00"}