{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YPJA5LOMOLGCAJXR2NBAEPAT5Q","short_pith_number":"pith:YPJA5LOM","schema_version":"1.0","canonical_sha256":"c3d20eadcc72cc2026f1d342023c13ec295d9337d12be21c4a799c2c50328c66","source":{"kind":"arxiv","id":"2506.14039","version":1},"attestation_state":"computed","paper":{"title":"Quadrotor Morpho-Transition: Learning vs Model-Based Control Strategies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Ioannis Mandralis, Morteza Gharib, Richard M. Murray","submitted_at":"2025-06-16T22:23:28Z","abstract_excerpt":"Quadrotor Morpho-Transition, or the act of transitioning from air to ground through mid-air transformation, involves complex aerodynamic interactions and a need to operate near actuator saturation, complicating controller design. In recent work, morpho-transition has been studied from a model-based control perspective, but these approaches remain limited due to unmodeled dynamics and the requirement for planning through contacts. Here, we train an end-to-end Reinforcement Learning (RL) controller to learn a morpho-transition policy and demonstrate successful transfer to hardware. We find that "},"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":"2506.14039","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-06-16T22:23:28Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"12f7efe4b0cb2290829c20f0a99548de63af63f89db4fc390be717d641bc405b","abstract_canon_sha256":"e792a27fd56ebc73fbfb773aa0fc225883b90fdd2f920fd3fc65ffc0eed2aa78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:47.899434Z","signature_b64":"5uQXceAgjQ5IM+RMHMwOUJ+0BG9ebZqyah8Z4j7S7clz8BLKDoBC12SSIWl5clER2A8xYnkYJAeptRWkCjCRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3d20eadcc72cc2026f1d342023c13ec295d9337d12be21c4a799c2c50328c66","last_reissued_at":"2026-07-05T11:22:47.898964Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:47.898964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quadrotor Morpho-Transition: Learning vs Model-Based Control Strategies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Ioannis Mandralis, Morteza Gharib, Richard M. Murray","submitted_at":"2025-06-16T22:23:28Z","abstract_excerpt":"Quadrotor Morpho-Transition, or the act of transitioning from air to ground through mid-air transformation, involves complex aerodynamic interactions and a need to operate near actuator saturation, complicating controller design. In recent work, morpho-transition has been studied from a model-based control perspective, but these approaches remain limited due to unmodeled dynamics and the requirement for planning through contacts. Here, we train an end-to-end Reinforcement Learning (RL) controller to learn a morpho-transition policy and demonstrate successful transfer to hardware. We find that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14039","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/2506.14039/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":"2506.14039","created_at":"2026-07-05T11:22:47.899023+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.14039v1","created_at":"2026-07-05T11:22:47.899023+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14039","created_at":"2026-07-05T11:22:47.899023+00:00"},{"alias_kind":"pith_short_12","alias_value":"YPJA5LOMOLGC","created_at":"2026-07-05T11:22:47.899023+00:00"},{"alias_kind":"pith_short_16","alias_value":"YPJA5LOMOLGCAJXR","created_at":"2026-07-05T11:22:47.899023+00:00"},{"alias_kind":"pith_short_8","alias_value":"YPJA5LOM","created_at":"2026-07-05T11:22:47.899023+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/YPJA5LOMOLGCAJXR2NBAEPAT5Q","json":"https://pith.science/pith/YPJA5LOMOLGCAJXR2NBAEPAT5Q.json","graph_json":"https://pith.science/api/pith-number/YPJA5LOMOLGCAJXR2NBAEPAT5Q/graph.json","events_json":"https://pith.science/api/pith-number/YPJA5LOMOLGCAJXR2NBAEPAT5Q/events.json","paper":"https://pith.science/paper/YPJA5LOM"},"agent_actions":{"view_html":"https://pith.science/pith/YPJA5LOMOLGCAJXR2NBAEPAT5Q","download_json":"https://pith.science/pith/YPJA5LOMOLGCAJXR2NBAEPAT5Q.json","view_paper":"https://pith.science/paper/YPJA5LOM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.14039&json=true","fetch_graph":"https://pith.science/api/pith-number/YPJA5LOMOLGCAJXR2NBAEPAT5Q/graph.json","fetch_events":"https://pith.science/api/pith-number/YPJA5LOMOLGCAJXR2NBAEPAT5Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YPJA5LOMOLGCAJXR2NBAEPAT5Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YPJA5LOMOLGCAJXR2NBAEPAT5Q/action/storage_attestation","attest_author":"https://pith.science/pith/YPJA5LOMOLGCAJXR2NBAEPAT5Q/action/author_attestation","sign_citation":"https://pith.science/pith/YPJA5LOMOLGCAJXR2NBAEPAT5Q/action/citation_signature","submit_replication":"https://pith.science/pith/YPJA5LOMOLGCAJXR2NBAEPAT5Q/action/replication_record"}},"created_at":"2026-07-05T11:22:47.899023+00:00","updated_at":"2026-07-05T11:22:47.899023+00:00"}