{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QF5FLIWRBMUNCJDOGSHJ5HJX4Q","short_pith_number":"pith:QF5FLIWR","schema_version":"1.0","canonical_sha256":"817a55a2d10b28d1246e348e9e9d37e40a6fa713cb5939a735c189396217e7a2","source":{"kind":"arxiv","id":"2503.12036","version":1},"attestation_state":"computed","paper":{"title":"Hierarchical Reinforcement Learning for Safe Mapless Navigation with Congestion Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Jianqi Gao, Qi Liu, Xizheng Pang, Yanjie Li","submitted_at":"2025-03-15T08:03:50Z","abstract_excerpt":"Reinforcement learning-based mapless navigation holds significant potential. However, it faces challenges in indoor environments with local minima area. This paper introduces a safe mapless navigation framework utilizing hierarchical reinforcement learning (HRL) to enhance navigation through such areas. The high-level policy creates a sub-goal to direct the navigation process. Notably, we have developed a sub-goal update mechanism that considers environment congestion, efficiently avoiding the entrapment of the robot in local minimum areas. The low-level motion planning policy, trained through"},"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":"2503.12036","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-03-15T08:03:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"057af8bb91e769b40a1af41c70446601c47e436bd32a7e19422408f0f231656a","abstract_canon_sha256":"1f0eb5837d30f7e8b155ce8c873b0c0117c707f43aa68599da82b78dc6322950"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:42.762853Z","signature_b64":"j0kaXO51IBcfpUYdbe7UxQCawnx+Tw1+FdU5zEONs8CTdblgBxyUgy9V+xJ8bZ3Qm/EwrhiVMN/nZ+NE0E3XAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"817a55a2d10b28d1246e348e9e9d37e40a6fa713cb5939a735c189396217e7a2","last_reissued_at":"2026-07-05T10:32:42.762253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:42.762253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Reinforcement Learning for Safe Mapless Navigation with Congestion Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Jianqi Gao, Qi Liu, Xizheng Pang, Yanjie Li","submitted_at":"2025-03-15T08:03:50Z","abstract_excerpt":"Reinforcement learning-based mapless navigation holds significant potential. However, it faces challenges in indoor environments with local minima area. This paper introduces a safe mapless navigation framework utilizing hierarchical reinforcement learning (HRL) to enhance navigation through such areas. The high-level policy creates a sub-goal to direct the navigation process. Notably, we have developed a sub-goal update mechanism that considers environment congestion, efficiently avoiding the entrapment of the robot in local minimum areas. The low-level motion planning policy, trained through"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.12036","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/2503.12036/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":"2503.12036","created_at":"2026-07-05T10:32:42.762324+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.12036v1","created_at":"2026-07-05T10:32:42.762324+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.12036","created_at":"2026-07-05T10:32:42.762324+00:00"},{"alias_kind":"pith_short_12","alias_value":"QF5FLIWRBMUN","created_at":"2026-07-05T10:32:42.762324+00:00"},{"alias_kind":"pith_short_16","alias_value":"QF5FLIWRBMUNCJDO","created_at":"2026-07-05T10:32:42.762324+00:00"},{"alias_kind":"pith_short_8","alias_value":"QF5FLIWR","created_at":"2026-07-05T10:32:42.762324+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.26504","citing_title":"HiPAN: Hierarchical Posture-Adaptive Navigation for Quadruped Robots in Unstructured 3D Environments","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q","json":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q.json","graph_json":"https://pith.science/api/pith-number/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/graph.json","events_json":"https://pith.science/api/pith-number/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/events.json","paper":"https://pith.science/paper/QF5FLIWR"},"agent_actions":{"view_html":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q","download_json":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q.json","view_paper":"https://pith.science/paper/QF5FLIWR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.12036&json=true","fetch_graph":"https://pith.science/api/pith-number/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/graph.json","fetch_events":"https://pith.science/api/pith-number/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/action/storage_attestation","attest_author":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/action/author_attestation","sign_citation":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/action/citation_signature","submit_replication":"https://pith.science/pith/QF5FLIWRBMUNCJDOGSHJ5HJX4Q/action/replication_record"}},"created_at":"2026-07-05T10:32:42.762324+00:00","updated_at":"2026-07-05T10:32:42.762324+00:00"}