{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:APJY2JGVFAY662CKVBWG7IQ3P4","short_pith_number":"pith:APJY2JGV","schema_version":"1.0","canonical_sha256":"03d38d24d52831ef684aa86c6fa21b7f05d9fd149ee5c5098ce8938d5bd3519f","source":{"kind":"arxiv","id":"2310.06585","version":2},"attestation_state":"computed","paper":{"title":"A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Alberto Dalla Libera, Diego Romeres, Giulio Giacomuzzos, Ruggero Carli","submitted_at":"2023-10-10T12:52:42Z","abstract_excerpt":"Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system is available. In this paper, we propose a black-box model based on Gaussian Process (GP) Regression for the identification of the inverse dynamics of robotic manipulators. The proposed model relies on a novel multidimensional kernel, called \\textit{Lagrangian Inspired Polynomial} (\\kernelInitials{}) kernel. The \\kernelInitials{} kernel is based on two main ideas. First, instead of directly modeling the inverse dynami"},"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":"2310.06585","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-10-10T12:52:42Z","cross_cats_sorted":["cs.AI","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"445068014c931eb008dc839cfabf86edfbe2c3746c23d09b8d2c54d0a5d06854","abstract_canon_sha256":"c119e1b98d21dec78c7406f5ebf149b534eb488ad2391c6fe258126885319bb2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:48.231698Z","signature_b64":"BqnjPQixung9qcGsQEMGXSQeLRAJaLd37bA7G3wbiLj0KXHwtWcR7DAo7Lic0FJuFCucFFBnD48pRhQkv23VAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03d38d24d52831ef684aa86c6fa21b7f05d9fd149ee5c5098ce8938d5bd3519f","last_reissued_at":"2026-07-05T09:03:48.231226Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:48.231226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Alberto Dalla Libera, Diego Romeres, Giulio Giacomuzzos, Ruggero Carli","submitted_at":"2023-10-10T12:52:42Z","abstract_excerpt":"Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system is available. In this paper, we propose a black-box model based on Gaussian Process (GP) Regression for the identification of the inverse dynamics of robotic manipulators. The proposed model relies on a novel multidimensional kernel, called \\textit{Lagrangian Inspired Polynomial} (\\kernelInitials{}) kernel. The \\kernelInitials{} kernel is based on two main ideas. First, instead of directly modeling the inverse dynami"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.06585","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/2310.06585/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":"2310.06585","created_at":"2026-07-05T09:03:48.231284+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.06585v2","created_at":"2026-07-05T09:03:48.231284+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.06585","created_at":"2026-07-05T09:03:48.231284+00:00"},{"alias_kind":"pith_short_12","alias_value":"APJY2JGVFAY6","created_at":"2026-07-05T09:03:48.231284+00:00"},{"alias_kind":"pith_short_16","alias_value":"APJY2JGVFAY662CK","created_at":"2026-07-05T09:03:48.231284+00:00"},{"alias_kind":"pith_short_8","alias_value":"APJY2JGV","created_at":"2026-07-05T09:03:48.231284+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/APJY2JGVFAY662CKVBWG7IQ3P4","json":"https://pith.science/pith/APJY2JGVFAY662CKVBWG7IQ3P4.json","graph_json":"https://pith.science/api/pith-number/APJY2JGVFAY662CKVBWG7IQ3P4/graph.json","events_json":"https://pith.science/api/pith-number/APJY2JGVFAY662CKVBWG7IQ3P4/events.json","paper":"https://pith.science/paper/APJY2JGV"},"agent_actions":{"view_html":"https://pith.science/pith/APJY2JGVFAY662CKVBWG7IQ3P4","download_json":"https://pith.science/pith/APJY2JGVFAY662CKVBWG7IQ3P4.json","view_paper":"https://pith.science/paper/APJY2JGV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.06585&json=true","fetch_graph":"https://pith.science/api/pith-number/APJY2JGVFAY662CKVBWG7IQ3P4/graph.json","fetch_events":"https://pith.science/api/pith-number/APJY2JGVFAY662CKVBWG7IQ3P4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/APJY2JGVFAY662CKVBWG7IQ3P4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/APJY2JGVFAY662CKVBWG7IQ3P4/action/storage_attestation","attest_author":"https://pith.science/pith/APJY2JGVFAY662CKVBWG7IQ3P4/action/author_attestation","sign_citation":"https://pith.science/pith/APJY2JGVFAY662CKVBWG7IQ3P4/action/citation_signature","submit_replication":"https://pith.science/pith/APJY2JGVFAY662CKVBWG7IQ3P4/action/replication_record"}},"created_at":"2026-07-05T09:03:48.231284+00:00","updated_at":"2026-07-05T09:03:48.231284+00:00"}