{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NSTF5CGCB4FFG5BZ7GCB376LAC","short_pith_number":"pith:NSTF5CGC","schema_version":"1.0","canonical_sha256":"6ca65e88c20f0a537439f9841dffcb00b79941e0fe1167521cea29050551af10","source":{"kind":"arxiv","id":"2505.24396","version":1},"attestation_state":"computed","paper":{"title":"Reactive Aerobatic Flight via Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fei Gao, Jiarui Zhang, Mingyang Wang, Tianyue Wu, Xijie Huang, Yuze Wu, Zhichao Han, Zhuxiu Xu","submitted_at":"2025-05-30T09:24:30Z","abstract_excerpt":"Quadrotors have demonstrated remarkable versatility, yet their full aerobatic potential remains largely untapped due to inherent underactuation and the complexity of aggressive maneuvers. Traditional approaches, separating trajectory optimization and tracking control, suffer from tracking inaccuracies, computational latency, and sensitivity to initial conditions, limiting their effectiveness in dynamic, high-agility scenarios. Inspired by recent breakthroughs in data-driven methods, we propose a reinforcement learning-based framework that directly maps drone states and aerobatic intentions to "},"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":"2505.24396","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-30T09:24:30Z","cross_cats_sorted":[],"title_canon_sha256":"e70537e71d7374de9b9ed842defac846593b37aa567cd5a5aa99b94ed2f22f6c","abstract_canon_sha256":"b78dbc0283261d8553516caddf751d5647018ca57ef1e3d3b2a0d9a932478a60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:48.292476Z","signature_b64":"ywa1CEfz73QfQyk5ATggmtm9oN+NkOgj2I1IOE+W2mb84wV60z7cGfJo1Gqn+ogj4J9PnxUfc6JXZnpWg7OqAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ca65e88c20f0a537439f9841dffcb00b79941e0fe1167521cea29050551af10","last_reissued_at":"2026-07-05T11:12:48.291890Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:48.291890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reactive Aerobatic Flight via Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fei Gao, Jiarui Zhang, Mingyang Wang, Tianyue Wu, Xijie Huang, Yuze Wu, Zhichao Han, Zhuxiu Xu","submitted_at":"2025-05-30T09:24:30Z","abstract_excerpt":"Quadrotors have demonstrated remarkable versatility, yet their full aerobatic potential remains largely untapped due to inherent underactuation and the complexity of aggressive maneuvers. Traditional approaches, separating trajectory optimization and tracking control, suffer from tracking inaccuracies, computational latency, and sensitivity to initial conditions, limiting their effectiveness in dynamic, high-agility scenarios. Inspired by recent breakthroughs in data-driven methods, we propose a reinforcement learning-based framework that directly maps drone states and aerobatic intentions to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24396","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/2505.24396/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":"2505.24396","created_at":"2026-07-05T11:12:48.291952+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.24396v1","created_at":"2026-07-05T11:12:48.291952+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24396","created_at":"2026-07-05T11:12:48.291952+00:00"},{"alias_kind":"pith_short_12","alias_value":"NSTF5CGCB4FF","created_at":"2026-07-05T11:12:48.291952+00:00"},{"alias_kind":"pith_short_16","alias_value":"NSTF5CGCB4FFG5BZ","created_at":"2026-07-05T11:12:48.291952+00:00"},{"alias_kind":"pith_short_8","alias_value":"NSTF5CGC","created_at":"2026-07-05T11:12:48.291952+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/NSTF5CGCB4FFG5BZ7GCB376LAC","json":"https://pith.science/pith/NSTF5CGCB4FFG5BZ7GCB376LAC.json","graph_json":"https://pith.science/api/pith-number/NSTF5CGCB4FFG5BZ7GCB376LAC/graph.json","events_json":"https://pith.science/api/pith-number/NSTF5CGCB4FFG5BZ7GCB376LAC/events.json","paper":"https://pith.science/paper/NSTF5CGC"},"agent_actions":{"view_html":"https://pith.science/pith/NSTF5CGCB4FFG5BZ7GCB376LAC","download_json":"https://pith.science/pith/NSTF5CGCB4FFG5BZ7GCB376LAC.json","view_paper":"https://pith.science/paper/NSTF5CGC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.24396&json=true","fetch_graph":"https://pith.science/api/pith-number/NSTF5CGCB4FFG5BZ7GCB376LAC/graph.json","fetch_events":"https://pith.science/api/pith-number/NSTF5CGCB4FFG5BZ7GCB376LAC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NSTF5CGCB4FFG5BZ7GCB376LAC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NSTF5CGCB4FFG5BZ7GCB376LAC/action/storage_attestation","attest_author":"https://pith.science/pith/NSTF5CGCB4FFG5BZ7GCB376LAC/action/author_attestation","sign_citation":"https://pith.science/pith/NSTF5CGCB4FFG5BZ7GCB376LAC/action/citation_signature","submit_replication":"https://pith.science/pith/NSTF5CGCB4FFG5BZ7GCB376LAC/action/replication_record"}},"created_at":"2026-07-05T11:12:48.291952+00:00","updated_at":"2026-07-05T11:12:48.291952+00:00"}