{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6DPKF3PPE3TCQQFGMDWZTS47XK","short_pith_number":"pith:6DPKF3PP","schema_version":"1.0","canonical_sha256":"f0dea2edef26e62840a660ed99cb9fba9da11a1825228f3de14408be59d4a80c","source":{"kind":"arxiv","id":"2308.13949","version":1},"attestation_state":"computed","paper":{"title":"Motion Planning as Online Learning: A Multi-Armed Bandit Approach to Kinodynamic Sampling-Based Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Dmitry Berenson, Marco Faroni","submitted_at":"2023-08-26T20:11:56Z","abstract_excerpt":"Kinodynamic motion planners allow robots to perform complex manipulation tasks under dynamics constraints or with black-box models. However, they struggle to find high-quality solutions, especially when a steering function is unavailable. This paper presents a novel approach that adaptively biases the sampling distribution to improve the planner's performance. The key contribution is to formulate the sampling bias problem as a non-stationary multi-armed bandit problem, where the arms of the bandit correspond to sets of possible transitions. High-reward regions are identified by clustering tran"},"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":"2308.13949","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-08-26T20:11:56Z","cross_cats_sorted":[],"title_canon_sha256":"36329c7860e4f779c771d389feabef63a3470b886aa7b937df3c6b241fe3e558","abstract_canon_sha256":"bc080da92e927ad89c31df46467f3664d39007897002d4d852d014df4e2571d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:45:07.534538Z","signature_b64":"BConSAVi3myv+bncyvrSW5YHzrdyve3oEacPjPsM0NdeOifazOflXrDL8bJ8SbPDlx919T5PAyqupd34CnPWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0dea2edef26e62840a660ed99cb9fba9da11a1825228f3de14408be59d4a80c","last_reissued_at":"2026-07-05T06:45:07.534130Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:45:07.534130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Motion Planning as Online Learning: A Multi-Armed Bandit Approach to Kinodynamic Sampling-Based Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Dmitry Berenson, Marco Faroni","submitted_at":"2023-08-26T20:11:56Z","abstract_excerpt":"Kinodynamic motion planners allow robots to perform complex manipulation tasks under dynamics constraints or with black-box models. However, they struggle to find high-quality solutions, especially when a steering function is unavailable. This paper presents a novel approach that adaptively biases the sampling distribution to improve the planner's performance. The key contribution is to formulate the sampling bias problem as a non-stationary multi-armed bandit problem, where the arms of the bandit correspond to sets of possible transitions. High-reward regions are identified by clustering tran"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.13949","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/2308.13949/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":"2308.13949","created_at":"2026-07-05T06:45:07.534188+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.13949v1","created_at":"2026-07-05T06:45:07.534188+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.13949","created_at":"2026-07-05T06:45:07.534188+00:00"},{"alias_kind":"pith_short_12","alias_value":"6DPKF3PPE3TC","created_at":"2026-07-05T06:45:07.534188+00:00"},{"alias_kind":"pith_short_16","alias_value":"6DPKF3PPE3TCQQFG","created_at":"2026-07-05T06:45:07.534188+00:00"},{"alias_kind":"pith_short_8","alias_value":"6DPKF3PP","created_at":"2026-07-05T06:45:07.534188+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/6DPKF3PPE3TCQQFGMDWZTS47XK","json":"https://pith.science/pith/6DPKF3PPE3TCQQFGMDWZTS47XK.json","graph_json":"https://pith.science/api/pith-number/6DPKF3PPE3TCQQFGMDWZTS47XK/graph.json","events_json":"https://pith.science/api/pith-number/6DPKF3PPE3TCQQFGMDWZTS47XK/events.json","paper":"https://pith.science/paper/6DPKF3PP"},"agent_actions":{"view_html":"https://pith.science/pith/6DPKF3PPE3TCQQFGMDWZTS47XK","download_json":"https://pith.science/pith/6DPKF3PPE3TCQQFGMDWZTS47XK.json","view_paper":"https://pith.science/paper/6DPKF3PP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.13949&json=true","fetch_graph":"https://pith.science/api/pith-number/6DPKF3PPE3TCQQFGMDWZTS47XK/graph.json","fetch_events":"https://pith.science/api/pith-number/6DPKF3PPE3TCQQFGMDWZTS47XK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6DPKF3PPE3TCQQFGMDWZTS47XK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6DPKF3PPE3TCQQFGMDWZTS47XK/action/storage_attestation","attest_author":"https://pith.science/pith/6DPKF3PPE3TCQQFGMDWZTS47XK/action/author_attestation","sign_citation":"https://pith.science/pith/6DPKF3PPE3TCQQFGMDWZTS47XK/action/citation_signature","submit_replication":"https://pith.science/pith/6DPKF3PPE3TCQQFGMDWZTS47XK/action/replication_record"}},"created_at":"2026-07-05T06:45:07.534188+00:00","updated_at":"2026-07-05T06:45:07.534188+00:00"}