{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:K3EENMFNZQNNVNH5DY6S7VXCR3","short_pith_number":"pith:K3EENMFN","schema_version":"1.0","canonical_sha256":"56c846b0adcc1adab4fd1e3d2fd6e28ef0d4240ca56828321b6c605352602c02","source":{"kind":"arxiv","id":"2302.03793","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Charles Averill, Kaiyu Hang, Kamalesh Palanisamy, Nicholas Ruozzi, Ninad Khargonkar, Yangxiao Lu, Yunhui Guo, Yu Xiang, Zesheng Xu","submitted_at":"2023-02-07T23:11:29Z","abstract_excerpt":"We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then obtain the segmentation mask of the grasped or pushed object after one action. Instead, our system defers the decision on segmenting objects after a sequence of robot pushing actions. By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-s"},"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":"2302.03793","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-02-07T23:11:29Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"fdfe70934ece24f1b7b3385669daf8a831e05731d01907b64df99e159bb396ec","abstract_canon_sha256":"481682e1def7313219a10d3d30155960dc22b58a510d45a3e4671df8d9d8ca2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:57.790693Z","signature_b64":"FKs85L7gF6jRGd6LBv9xWA+62XH/9CZXGRcWyOZu6/mX79ef7XgmJVvx+EDYshWW4y3MYhGtbi7cKOqd2KVmCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56c846b0adcc1adab4fd1e3d2fd6e28ef0d4240ca56828321b6c605352602c02","last_reissued_at":"2026-07-05T05:39:57.790126Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:57.790126Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Charles Averill, Kaiyu Hang, Kamalesh Palanisamy, Nicholas Ruozzi, Ninad Khargonkar, Yangxiao Lu, Yunhui Guo, Yu Xiang, Zesheng Xu","submitted_at":"2023-02-07T23:11:29Z","abstract_excerpt":"We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then obtain the segmentation mask of the grasped or pushed object after one action. Instead, our system defers the decision on segmenting objects after a sequence of robot pushing actions. By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03793","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.03793/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":"2302.03793","created_at":"2026-07-05T05:39:57.790185+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.03793v1","created_at":"2026-07-05T05:39:57.790185+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03793","created_at":"2026-07-05T05:39:57.790185+00:00"},{"alias_kind":"pith_short_12","alias_value":"K3EENMFNZQNN","created_at":"2026-07-05T05:39:57.790185+00:00"},{"alias_kind":"pith_short_16","alias_value":"K3EENMFNZQNNVNH5","created_at":"2026-07-05T05:39:57.790185+00:00"},{"alias_kind":"pith_short_8","alias_value":"K3EENMFN","created_at":"2026-07-05T05:39:57.790185+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.03266","citing_title":"ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3","json":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3.json","graph_json":"https://pith.science/api/pith-number/K3EENMFNZQNNVNH5DY6S7VXCR3/graph.json","events_json":"https://pith.science/api/pith-number/K3EENMFNZQNNVNH5DY6S7VXCR3/events.json","paper":"https://pith.science/paper/K3EENMFN"},"agent_actions":{"view_html":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3","download_json":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3.json","view_paper":"https://pith.science/paper/K3EENMFN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.03793&json=true","fetch_graph":"https://pith.science/api/pith-number/K3EENMFNZQNNVNH5DY6S7VXCR3/graph.json","fetch_events":"https://pith.science/api/pith-number/K3EENMFNZQNNVNH5DY6S7VXCR3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3/action/storage_attestation","attest_author":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3/action/author_attestation","sign_citation":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3/action/citation_signature","submit_replication":"https://pith.science/pith/K3EENMFNZQNNVNH5DY6S7VXCR3/action/replication_record"}},"created_at":"2026-07-05T05:39:57.790185+00:00","updated_at":"2026-07-05T05:39:57.790185+00:00"}