{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6IKXV6NWWJSU6CJIR4PD6ZRVPZ","short_pith_number":"pith:6IKXV6NW","schema_version":"1.0","canonical_sha256":"f2157af9b6b2654f09288f1e3f66357e6b49c715303d2169ca22b97e4d72e756","source":{"kind":"arxiv","id":"2601.15614","version":2},"attestation_state":"computed","paper":{"title":"AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Lin Zhao, Rui Huang, Shenao Wang, Yichao Gao, Yuchen Hou, Zichen Yan","submitted_at":"2026-01-22T03:35:34Z","abstract_excerpt":"Object-Goal Navigation (ObjectNav) requires an agent to autonomously explore an unknown environment and navigate toward target objects specified by a semantic label. While prior work has primarily studied zero-shot ObjectNav under 2D locomotion, extending it to aerial platforms with 3D locomotion capability remains underexplored. Aerial robots offer superior maneuverability and search efficiency, but they also introduce new challenges in spatial perception, dynamic control, and safety assurance. In this paper, we propose AION for vision-based aerial ObjectNav without relying on external locali"},"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":"2601.15614","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-01-22T03:35:34Z","cross_cats_sorted":[],"title_canon_sha256":"7310f323bad81c7ebf5e5f5a91f9dc8bc6dc9ff72723e4425a1dcba52a4ae7f3","abstract_canon_sha256":"205850ef67ccd7d55379b4a751a74c58cf12256e35861cfe46a147c5b3c72c42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:50.471235Z","signature_b64":"dXR0KkgQOfeXKrI9y32ncJ6RjJ7MljZswQ7vsdObj4aAzSstey33uQCluHM42hIF+8PaKVjnFpmwGkOp62LRBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2157af9b6b2654f09288f1e3f66357e6b49c715303d2169ca22b97e4d72e756","last_reissued_at":"2026-06-19T16:12:50.470735Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:50.470735Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Lin Zhao, Rui Huang, Shenao Wang, Yichao Gao, Yuchen Hou, Zichen Yan","submitted_at":"2026-01-22T03:35:34Z","abstract_excerpt":"Object-Goal Navigation (ObjectNav) requires an agent to autonomously explore an unknown environment and navigate toward target objects specified by a semantic label. While prior work has primarily studied zero-shot ObjectNav under 2D locomotion, extending it to aerial platforms with 3D locomotion capability remains underexplored. Aerial robots offer superior maneuverability and search efficiency, but they also introduce new challenges in spatial perception, dynamic control, and safety assurance. In this paper, we propose AION for vision-based aerial ObjectNav without relying on external locali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.15614","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.15614/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":"2601.15614","created_at":"2026-06-19T16:12:50.470804+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.15614v2","created_at":"2026-06-19T16:12:50.470804+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.15614","created_at":"2026-06-19T16:12:50.470804+00:00"},{"alias_kind":"pith_short_12","alias_value":"6IKXV6NWWJSU","created_at":"2026-06-19T16:12:50.470804+00:00"},{"alias_kind":"pith_short_16","alias_value":"6IKXV6NWWJSU6CJI","created_at":"2026-06-19T16:12:50.470804+00:00"},{"alias_kind":"pith_short_8","alias_value":"6IKXV6NW","created_at":"2026-06-19T16:12:50.470804+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2604.07705","citing_title":"Vision-Language Navigation for Aerial Robots: Towards the Era of Large Language Models","ref_index":146,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ","json":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ.json","graph_json":"https://pith.science/api/pith-number/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/graph.json","events_json":"https://pith.science/api/pith-number/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/events.json","paper":"https://pith.science/paper/6IKXV6NW"},"agent_actions":{"view_html":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ","download_json":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ.json","view_paper":"https://pith.science/paper/6IKXV6NW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.15614&json=true","fetch_graph":"https://pith.science/api/pith-number/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/graph.json","fetch_events":"https://pith.science/api/pith-number/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/action/storage_attestation","attest_author":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/action/author_attestation","sign_citation":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/action/citation_signature","submit_replication":"https://pith.science/pith/6IKXV6NWWJSU6CJIR4PD6ZRVPZ/action/replication_record"}},"created_at":"2026-06-19T16:12:50.470804+00:00","updated_at":"2026-06-19T16:12:50.470804+00:00"}