{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZR3UJDCAJXNLBM5YABIEKHCUYG","short_pith_number":"pith:ZR3UJDCA","schema_version":"1.0","canonical_sha256":"cc77448c404ddab0b3b80050451c54c1b7a3a8d03fda3d84f5c9f7a610fb890f","source":{"kind":"arxiv","id":"2408.08830","version":1},"attestation_state":"computed","paper":{"title":"System Identification For Constrained Robots","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bohao Zhang, Daniel Haugk, Ram Vasudevan","submitted_at":"2024-08-16T16:27:02Z","abstract_excerpt":"Identifying the parameters of robotic systems, such as motor inertia or joint friction, is critical to satisfactory controller synthesis, model analysis, and observer design. Conventional identification techniques are designed primarily for unconstrained systems, such as robotic manipulators. In contrast, the growing importance of legged robots that feature closed kinematic chains or other constraints, poses challenges to these traditional methods. This paper introduces a system identification approach for constrained systems that relies on iterative least squares to identify motor inertia and"},"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":"2408.08830","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-08-16T16:27:02Z","cross_cats_sorted":[],"title_canon_sha256":"c973a768f3d21d39d4e3ae6a43f67a1d19973c9dd3c1e54ee609bf47a6b5539c","abstract_canon_sha256":"9e01c36d98dde8880a58ae0cdee13860d0bc72e677543b60de496c6b0b6661e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:56:09.353073Z","signature_b64":"fuCuHi5RsfwYDjxTeN1SnIcR1ASV7CsCgfcAFP/xsBs8iTKBGv9jc2CwZ4UQdfM3DEefNxIT7ryyv69JAy1hAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc77448c404ddab0b3b80050451c54c1b7a3a8d03fda3d84f5c9f7a610fb890f","last_reissued_at":"2026-07-05T08:56:09.352614Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:56:09.352614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"System Identification For Constrained Robots","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bohao Zhang, Daniel Haugk, Ram Vasudevan","submitted_at":"2024-08-16T16:27:02Z","abstract_excerpt":"Identifying the parameters of robotic systems, such as motor inertia or joint friction, is critical to satisfactory controller synthesis, model analysis, and observer design. Conventional identification techniques are designed primarily for unconstrained systems, such as robotic manipulators. In contrast, the growing importance of legged robots that feature closed kinematic chains or other constraints, poses challenges to these traditional methods. This paper introduces a system identification approach for constrained systems that relies on iterative least squares to identify motor inertia and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08830","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/2408.08830/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":"2408.08830","created_at":"2026-07-05T08:56:09.352670+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.08830v1","created_at":"2026-07-05T08:56:09.352670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08830","created_at":"2026-07-05T08:56:09.352670+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZR3UJDCAJXNL","created_at":"2026-07-05T08:56:09.352670+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZR3UJDCAJXNLBM5Y","created_at":"2026-07-05T08:56:09.352670+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZR3UJDCA","created_at":"2026-07-05T08:56:09.352670+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16520","citing_title":"Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing","ref_index":237,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG","json":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG.json","graph_json":"https://pith.science/api/pith-number/ZR3UJDCAJXNLBM5YABIEKHCUYG/graph.json","events_json":"https://pith.science/api/pith-number/ZR3UJDCAJXNLBM5YABIEKHCUYG/events.json","paper":"https://pith.science/paper/ZR3UJDCA"},"agent_actions":{"view_html":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG","download_json":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG.json","view_paper":"https://pith.science/paper/ZR3UJDCA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.08830&json=true","fetch_graph":"https://pith.science/api/pith-number/ZR3UJDCAJXNLBM5YABIEKHCUYG/graph.json","fetch_events":"https://pith.science/api/pith-number/ZR3UJDCAJXNLBM5YABIEKHCUYG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG/action/storage_attestation","attest_author":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG/action/author_attestation","sign_citation":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG/action/citation_signature","submit_replication":"https://pith.science/pith/ZR3UJDCAJXNLBM5YABIEKHCUYG/action/replication_record"}},"created_at":"2026-07-05T08:56:09.352670+00:00","updated_at":"2026-07-05T08:56:09.352670+00:00"}