{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TSEQYDO5CAG3BUVSU4NPNJIM5W","short_pith_number":"pith:TSEQYDO5","schema_version":"1.0","canonical_sha256":"9c890c0ddd100db0d2b2a71af6a50ced8031974eaa9de0e6c38451f801703d22","source":{"kind":"arxiv","id":"2301.13183","version":1},"attestation_state":"computed","paper":{"title":"Learning Control from Raw Position Measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Alberto Dalla Libera, Daniel Nikovski, Diego Romeres, Fabio Amadio, Ruggero Carli","submitted_at":"2023-01-30T18:50:37Z","abstract_excerpt":"We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly measured. This circumstance, if not adequately considered, can compromise the success of MBRL approaches. To cope with this problem, we define a velocity-free state formulation which consists of the collection of past positions and inputs. Then, VF-MC-PILCO uses Gaussian Process Regression to model the dynamics of the velocity-free state and optimizes the control policy through a particle-based policy gradient approa"},"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":"2301.13183","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-01-30T18:50:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7c6c6ada02674e024ad8bffa604b52be0cf2cc4eff46eb03e8ef0b0f41ed0ace","abstract_canon_sha256":"2a4455d2ea9283cd18ed76facfcb778bbf575fe15248d0dd2bbb373c8111cf19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:37:01.569902Z","signature_b64":"GBGr5SAKMycqBYITvomYJlNlxsZQyMmF3qq6hmgt+B9wjTHnvhYm/vMBwkHG6lnUBCyUJafMCKRnRZTdoeO0BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c890c0ddd100db0d2b2a71af6a50ced8031974eaa9de0e6c38451f801703d22","last_reissued_at":"2026-07-05T05:37:01.569430Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:37:01.569430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Control from Raw Position Measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Alberto Dalla Libera, Daniel Nikovski, Diego Romeres, Fabio Amadio, Ruggero Carli","submitted_at":"2023-01-30T18:50:37Z","abstract_excerpt":"We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly measured. This circumstance, if not adequately considered, can compromise the success of MBRL approaches. To cope with this problem, we define a velocity-free state formulation which consists of the collection of past positions and inputs. Then, VF-MC-PILCO uses Gaussian Process Regression to model the dynamics of the velocity-free state and optimizes the control policy through a particle-based policy gradient approa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13183","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/2301.13183/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":"2301.13183","created_at":"2026-07-05T05:37:01.569489+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.13183v1","created_at":"2026-07-05T05:37:01.569489+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13183","created_at":"2026-07-05T05:37:01.569489+00:00"},{"alias_kind":"pith_short_12","alias_value":"TSEQYDO5CAG3","created_at":"2026-07-05T05:37:01.569489+00:00"},{"alias_kind":"pith_short_16","alias_value":"TSEQYDO5CAG3BUVS","created_at":"2026-07-05T05:37:01.569489+00:00"},{"alias_kind":"pith_short_8","alias_value":"TSEQYDO5","created_at":"2026-07-05T05:37:01.569489+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/TSEQYDO5CAG3BUVSU4NPNJIM5W","json":"https://pith.science/pith/TSEQYDO5CAG3BUVSU4NPNJIM5W.json","graph_json":"https://pith.science/api/pith-number/TSEQYDO5CAG3BUVSU4NPNJIM5W/graph.json","events_json":"https://pith.science/api/pith-number/TSEQYDO5CAG3BUVSU4NPNJIM5W/events.json","paper":"https://pith.science/paper/TSEQYDO5"},"agent_actions":{"view_html":"https://pith.science/pith/TSEQYDO5CAG3BUVSU4NPNJIM5W","download_json":"https://pith.science/pith/TSEQYDO5CAG3BUVSU4NPNJIM5W.json","view_paper":"https://pith.science/paper/TSEQYDO5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.13183&json=true","fetch_graph":"https://pith.science/api/pith-number/TSEQYDO5CAG3BUVSU4NPNJIM5W/graph.json","fetch_events":"https://pith.science/api/pith-number/TSEQYDO5CAG3BUVSU4NPNJIM5W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TSEQYDO5CAG3BUVSU4NPNJIM5W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TSEQYDO5CAG3BUVSU4NPNJIM5W/action/storage_attestation","attest_author":"https://pith.science/pith/TSEQYDO5CAG3BUVSU4NPNJIM5W/action/author_attestation","sign_citation":"https://pith.science/pith/TSEQYDO5CAG3BUVSU4NPNJIM5W/action/citation_signature","submit_replication":"https://pith.science/pith/TSEQYDO5CAG3BUVSU4NPNJIM5W/action/replication_record"}},"created_at":"2026-07-05T05:37:01.569489+00:00","updated_at":"2026-07-05T05:37:01.569489+00:00"}