{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XDNQKS2OS5UFLI57MT7A7NQVEA","short_pith_number":"pith:XDNQKS2O","schema_version":"1.0","canonical_sha256":"b8db054b4e976855a3bf64fe0fb615201536cff7ee7dd00f53b4bb568b2ad6d6","source":{"kind":"arxiv","id":"1902.09157","version":1},"attestation_state":"computed","paper":{"title":"Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Joshua C. Triyonoputro, Kensuke Harada, Weiwei Wan","submitted_at":"2019-02-25T09:20:20Z","abstract_excerpt":"This paper uses robots to assemble pegs into holes on surfaces with different colors and textures. It especially targets at the problem of peg-in-hole assembly with initial position uncertainty. Two in-hand cameras and a force-torque sensor are used to account for the position uncertainty. A program sequence comprising learning-based visual servoing, spiral search, and impedance control is implemented to perform the peg-in-hole task with feedback from the above sensors. Contributions are mainly made in the learning-based visual servoing of the sequence, where a deep neural network is trained w"},"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":"1902.09157","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-02-25T09:20:20Z","cross_cats_sorted":[],"title_canon_sha256":"08450ad0c613efc2605ff84e5b972c52b25904df5d6dcea62abc1a63fdda4b43","abstract_canon_sha256":"88ae47bf5621c003d58345430360447611b93229ef51673f25fbb140f6ee2132"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:46.492925Z","signature_b64":"unsTU2BXEjpSAulJwx6AeiGLjgIAeSeFKA7aLu9gFzryD9ojcFqsQSl8biheyAqeUR9t5z6u63zSHm1tjEbYDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8db054b4e976855a3bf64fe0fb615201536cff7ee7dd00f53b4bb568b2ad6d6","last_reissued_at":"2026-05-17T23:52:46.492391Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:46.492391Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Joshua C. Triyonoputro, Kensuke Harada, Weiwei Wan","submitted_at":"2019-02-25T09:20:20Z","abstract_excerpt":"This paper uses robots to assemble pegs into holes on surfaces with different colors and textures. It especially targets at the problem of peg-in-hole assembly with initial position uncertainty. Two in-hand cameras and a force-torque sensor are used to account for the position uncertainty. A program sequence comprising learning-based visual servoing, spiral search, and impedance control is implemented to perform the peg-in-hole task with feedback from the above sensors. Contributions are mainly made in the learning-based visual servoing of the sequence, where a deep neural network is trained w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09157","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":"1902.09157","created_at":"2026-05-17T23:52:46.492497+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.09157v1","created_at":"2026-05-17T23:52:46.492497+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09157","created_at":"2026-05-17T23:52:46.492497+00:00"},{"alias_kind":"pith_short_12","alias_value":"XDNQKS2OS5UF","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"XDNQKS2OS5UFLI57","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"XDNQKS2O","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA","json":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA.json","graph_json":"https://pith.science/api/pith-number/XDNQKS2OS5UFLI57MT7A7NQVEA/graph.json","events_json":"https://pith.science/api/pith-number/XDNQKS2OS5UFLI57MT7A7NQVEA/events.json","paper":"https://pith.science/paper/XDNQKS2O"},"agent_actions":{"view_html":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA","download_json":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA.json","view_paper":"https://pith.science/paper/XDNQKS2O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.09157&json=true","fetch_graph":"https://pith.science/api/pith-number/XDNQKS2OS5UFLI57MT7A7NQVEA/graph.json","fetch_events":"https://pith.science/api/pith-number/XDNQKS2OS5UFLI57MT7A7NQVEA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA/action/storage_attestation","attest_author":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA/action/author_attestation","sign_citation":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA/action/citation_signature","submit_replication":"https://pith.science/pith/XDNQKS2OS5UFLI57MT7A7NQVEA/action/replication_record"}},"created_at":"2026-05-17T23:52:46.492497+00:00","updated_at":"2026-05-17T23:52:46.492497+00:00"}