{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PFR55R7NPH5PVXPLX7QBOIJCZY","short_pith_number":"pith:PFR55R7N","schema_version":"1.0","canonical_sha256":"7963dec7ed79fafaddebbfe0172122ce176eb52e8849a8d14af1ef111515c2d6","source":{"kind":"arxiv","id":"2504.04936","version":1},"attestation_state":"computed","paper":{"title":"Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"An Thai Le, Georgia Chalvatzaki, Haolei Tong, Jan Peters, Jiayun Li, Kay Pompetzki","submitted_at":"2025-04-07T11:20:11Z","abstract_excerpt":"Gaussian Process Motion Planning (GPMP) is a widely used framework for generating smooth trajectories within a limited compute time--an essential requirement in many robotic applications. However, traditional GPMP approaches often struggle with enforcing hard nonlinear constraints and rely on Maximum a Posteriori (MAP) solutions that disregard the full Bayesian posterior. This limits planning diversity and ultimately hampers decision-making. Recent efforts to integrate Stein Variational Gradient Descent (SVGD) into motion planning have shown promise in handling complex constraints. Nonetheless"},"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":"2504.04936","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-04-07T11:20:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b3255f6e3b7575924eca6e5c79bbc4605f355bc16d828c9102d94b3014027e27","abstract_canon_sha256":"43dd61f05a7702f275284c1d2bebb3b031131eccd85fbddcb5a81a99efea6e05"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:45:31.766885Z","signature_b64":"G581SDFJ8dIOcNe80lazBlmP9EXH9AZegk8wcy9p95Ae65hCYfuJZ1Q5K0/2GXxwe4D12+8QmpMDE1igvmFMAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7963dec7ed79fafaddebbfe0172122ce176eb52e8849a8d14af1ef111515c2d6","last_reissued_at":"2026-07-05T10:45:31.766413Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:45:31.766413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"An Thai Le, Georgia Chalvatzaki, Haolei Tong, Jan Peters, Jiayun Li, Kay Pompetzki","submitted_at":"2025-04-07T11:20:11Z","abstract_excerpt":"Gaussian Process Motion Planning (GPMP) is a widely used framework for generating smooth trajectories within a limited compute time--an essential requirement in many robotic applications. However, traditional GPMP approaches often struggle with enforcing hard nonlinear constraints and rely on Maximum a Posteriori (MAP) solutions that disregard the full Bayesian posterior. This limits planning diversity and ultimately hampers decision-making. Recent efforts to integrate Stein Variational Gradient Descent (SVGD) into motion planning have shown promise in handling complex constraints. Nonetheless"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.04936","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/2504.04936/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":"2504.04936","created_at":"2026-07-05T10:45:31.766472+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.04936v1","created_at":"2026-07-05T10:45:31.766472+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.04936","created_at":"2026-07-05T10:45:31.766472+00:00"},{"alias_kind":"pith_short_12","alias_value":"PFR55R7NPH5P","created_at":"2026-07-05T10:45:31.766472+00:00"},{"alias_kind":"pith_short_16","alias_value":"PFR55R7NPH5PVXPL","created_at":"2026-07-05T10:45:31.766472+00:00"},{"alias_kind":"pith_short_8","alias_value":"PFR55R7N","created_at":"2026-07-05T10:45:31.766472+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/PFR55R7NPH5PVXPLX7QBOIJCZY","json":"https://pith.science/pith/PFR55R7NPH5PVXPLX7QBOIJCZY.json","graph_json":"https://pith.science/api/pith-number/PFR55R7NPH5PVXPLX7QBOIJCZY/graph.json","events_json":"https://pith.science/api/pith-number/PFR55R7NPH5PVXPLX7QBOIJCZY/events.json","paper":"https://pith.science/paper/PFR55R7N"},"agent_actions":{"view_html":"https://pith.science/pith/PFR55R7NPH5PVXPLX7QBOIJCZY","download_json":"https://pith.science/pith/PFR55R7NPH5PVXPLX7QBOIJCZY.json","view_paper":"https://pith.science/paper/PFR55R7N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.04936&json=true","fetch_graph":"https://pith.science/api/pith-number/PFR55R7NPH5PVXPLX7QBOIJCZY/graph.json","fetch_events":"https://pith.science/api/pith-number/PFR55R7NPH5PVXPLX7QBOIJCZY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PFR55R7NPH5PVXPLX7QBOIJCZY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PFR55R7NPH5PVXPLX7QBOIJCZY/action/storage_attestation","attest_author":"https://pith.science/pith/PFR55R7NPH5PVXPLX7QBOIJCZY/action/author_attestation","sign_citation":"https://pith.science/pith/PFR55R7NPH5PVXPLX7QBOIJCZY/action/citation_signature","submit_replication":"https://pith.science/pith/PFR55R7NPH5PVXPLX7QBOIJCZY/action/replication_record"}},"created_at":"2026-07-05T10:45:31.766472+00:00","updated_at":"2026-07-05T10:45:31.766472+00:00"}