{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:225YLHREPFZDMDZSCJWW7RQOLB","short_pith_number":"pith:225YLHRE","schema_version":"1.0","canonical_sha256":"d6bb859e247972360f32126d6fc60e5858f609e4722cda05eac3c7818aece3a3","source":{"kind":"arxiv","id":"1705.08049","version":1},"attestation_state":"computed","paper":{"title":"Neural Network Memory Architectures for Autonomous Robot Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Arbaaz Khan, Daniel D. Lee, Konstantinos Karydis, Nikolay Atanasov, Steven W Chen, Vijay Kumar","submitted_at":"2017-05-23T00:58:20Z","abstract_excerpt":"This paper highlights the significance of including memory structures in neural networks when the latter are used to learn perception-action loops for autonomous robot navigation. Traditional navigation approaches rely on global maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet, maintaining an accurate global map may be challenging in real-world settings. A possible way to mitigate this limitation is to use learning techniques that forgo hand-engineered map representations and infer appropriate control responses directly from sensed information. An important but un"},"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":"1705.08049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-05-23T00:58:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6c7e18956a7173ff2a782390d7aa8c881f442ffda5be59b315e057b8feb110d1","abstract_canon_sha256":"be6decea73fbbfc370aecee91d61e9e7faca6ebbd65f662085ca1c5f74d5d5af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:49.482142Z","signature_b64":"HemMeHerxBv+7k2woz0NzjGrpCKTLTb5IP2RfuUIv516+BPInV2DTNW0jf8zKvXYYf7gqYFwkdUH1BM7AXZbBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6bb859e247972360f32126d6fc60e5858f609e4722cda05eac3c7818aece3a3","last_reissued_at":"2026-05-18T00:43:49.481533Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:49.481533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Network Memory Architectures for Autonomous Robot Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Arbaaz Khan, Daniel D. Lee, Konstantinos Karydis, Nikolay Atanasov, Steven W Chen, Vijay Kumar","submitted_at":"2017-05-23T00:58:20Z","abstract_excerpt":"This paper highlights the significance of including memory structures in neural networks when the latter are used to learn perception-action loops for autonomous robot navigation. Traditional navigation approaches rely on global maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet, maintaining an accurate global map may be challenging in real-world settings. A possible way to mitigate this limitation is to use learning techniques that forgo hand-engineered map representations and infer appropriate control responses directly from sensed information. An important but un"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08049","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":"1705.08049","created_at":"2026-05-18T00:43:49.481627+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.08049v1","created_at":"2026-05-18T00:43:49.481627+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08049","created_at":"2026-05-18T00:43:49.481627+00:00"},{"alias_kind":"pith_short_12","alias_value":"225YLHREPFZD","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"225YLHREPFZDMDZS","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"225YLHRE","created_at":"2026-05-18T12:30:55.937587+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/225YLHREPFZDMDZSCJWW7RQOLB","json":"https://pith.science/pith/225YLHREPFZDMDZSCJWW7RQOLB.json","graph_json":"https://pith.science/api/pith-number/225YLHREPFZDMDZSCJWW7RQOLB/graph.json","events_json":"https://pith.science/api/pith-number/225YLHREPFZDMDZSCJWW7RQOLB/events.json","paper":"https://pith.science/paper/225YLHRE"},"agent_actions":{"view_html":"https://pith.science/pith/225YLHREPFZDMDZSCJWW7RQOLB","download_json":"https://pith.science/pith/225YLHREPFZDMDZSCJWW7RQOLB.json","view_paper":"https://pith.science/paper/225YLHRE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.08049&json=true","fetch_graph":"https://pith.science/api/pith-number/225YLHREPFZDMDZSCJWW7RQOLB/graph.json","fetch_events":"https://pith.science/api/pith-number/225YLHREPFZDMDZSCJWW7RQOLB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/225YLHREPFZDMDZSCJWW7RQOLB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/225YLHREPFZDMDZSCJWW7RQOLB/action/storage_attestation","attest_author":"https://pith.science/pith/225YLHREPFZDMDZSCJWW7RQOLB/action/author_attestation","sign_citation":"https://pith.science/pith/225YLHREPFZDMDZSCJWW7RQOLB/action/citation_signature","submit_replication":"https://pith.science/pith/225YLHREPFZDMDZSCJWW7RQOLB/action/replication_record"}},"created_at":"2026-05-18T00:43:49.481627+00:00","updated_at":"2026-05-18T00:43:49.481627+00:00"}