{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YL6ABK4DRO5WAAW6ZS3B4OPOV6","short_pith_number":"pith:YL6ABK4D","schema_version":"1.0","canonical_sha256":"c2fc00ab838bbb6002deccb61e39eeaf9bf7bb589e48c49897899f24a08f2e81","source":{"kind":"arxiv","id":"1810.00804","version":1},"attestation_state":"computed","paper":{"title":"Deep sequential models for sampling-based planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andrei Barbu, Boris Katz, Yen-Ling Kuo","submitted_at":"2018-10-01T16:37:52Z","abstract_excerpt":"We demonstrate how a sequence model and a sampling-based planner can influence each other to produce efficient plans and how such a model can automatically learn to take advantage of observations of the environment. Sampling-based planners such as RRT generally know nothing of their environments even if they have traversed similar spaces many times. A sequence model, such as an HMM or LSTM, guides the search for good paths. The resulting model, called DeRRT*, observes the state of the planner and the local environment to bias the next move and next planner state. The neural-network-based model"},"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":"1810.00804","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-10-01T16:37:52Z","cross_cats_sorted":[],"title_canon_sha256":"3d00d1ab8b3f128c131fcad8b6de0a9ac0e7ecc35d29576d584f417528ff5a57","abstract_canon_sha256":"59ecf54b6e595fca48500aeb7933deae00130a619af974a224f34aa717d6b331"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:24.788928Z","signature_b64":"j2/18RnVaSlOLY1gDazV534nzRrfV1ADmFEPCwEAibs8HxQvWXSWAQZ4ZdiMUtbZyPAJB3pHIh4mDqLXxb4PDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2fc00ab838bbb6002deccb61e39eeaf9bf7bb589e48c49897899f24a08f2e81","last_reissued_at":"2026-05-18T00:04:24.788402Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:24.788402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep sequential models for sampling-based planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andrei Barbu, Boris Katz, Yen-Ling Kuo","submitted_at":"2018-10-01T16:37:52Z","abstract_excerpt":"We demonstrate how a sequence model and a sampling-based planner can influence each other to produce efficient plans and how such a model can automatically learn to take advantage of observations of the environment. Sampling-based planners such as RRT generally know nothing of their environments even if they have traversed similar spaces many times. A sequence model, such as an HMM or LSTM, guides the search for good paths. The resulting model, called DeRRT*, observes the state of the planner and the local environment to bias the next move and next planner state. The neural-network-based model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00804","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":""},"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":"1810.00804","created_at":"2026-05-18T00:04:24.788483+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.00804v1","created_at":"2026-05-18T00:04:24.788483+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00804","created_at":"2026-05-18T00:04:24.788483+00:00"},{"alias_kind":"pith_short_12","alias_value":"YL6ABK4DRO5W","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YL6ABK4DRO5WAAW6","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YL6ABK4D","created_at":"2026-05-18T12:33:04.347982+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.09574","citing_title":"LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6","json":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6.json","graph_json":"https://pith.science/api/pith-number/YL6ABK4DRO5WAAW6ZS3B4OPOV6/graph.json","events_json":"https://pith.science/api/pith-number/YL6ABK4DRO5WAAW6ZS3B4OPOV6/events.json","paper":"https://pith.science/paper/YL6ABK4D"},"agent_actions":{"view_html":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6","download_json":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6.json","view_paper":"https://pith.science/paper/YL6ABK4D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.00804&json=true","fetch_graph":"https://pith.science/api/pith-number/YL6ABK4DRO5WAAW6ZS3B4OPOV6/graph.json","fetch_events":"https://pith.science/api/pith-number/YL6ABK4DRO5WAAW6ZS3B4OPOV6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6/action/storage_attestation","attest_author":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6/action/author_attestation","sign_citation":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6/action/citation_signature","submit_replication":"https://pith.science/pith/YL6ABK4DRO5WAAW6ZS3B4OPOV6/action/replication_record"}},"created_at":"2026-05-18T00:04:24.788483+00:00","updated_at":"2026-05-18T00:04:24.788483+00:00"}