{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:X3HCLL4RRC2EUHJZRYGINE35CR","short_pith_number":"pith:X3HCLL4R","schema_version":"1.0","canonical_sha256":"bece25af9188b44a1d398e0c86937d1464bbacb8e86272711525f7bd6ce51898","source":{"kind":"arxiv","id":"2411.14386","version":1},"attestation_state":"computed","paper":{"title":"Learning Humanoid Locomotion with Perceptive Internal Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jiangmiao Pang, Junfeng Long, Junli Ren, Moji Shi, Ping Luo, Tao Huang, Zirui Wang","submitted_at":"2024-11-21T18:21:59Z","abstract_excerpt":"In contrast to quadruped robots that can navigate diverse terrains using a \"blind\" policy, humanoid robots require accurate perception for stable locomotion due to their high degrees of freedom and inherently unstable morphology. However, incorporating perceptual signals often introduces additional disturbances to the system, potentially reducing its robustness, generalizability, and efficiency. This paper presents the Perceptive Internal Model (PIM), which relies on onboard, continuously updated elevation maps centered around the robot to perceive its surroundings. We train the policy using g"},"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":"2411.14386","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-11-21T18:21:59Z","cross_cats_sorted":[],"title_canon_sha256":"394155f67a9f2873ce3633cc89fb4fc6c66fde335f2070c25e1834db57bcbf41","abstract_canon_sha256":"a202b611f5bd39dc6b2dc5c29d0022c29e1f0805aa1c0859166d1a54095dea62"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:46.179524Z","signature_b64":"D9O1Q2u16FhTQ8wiIFlKhWnY3yt3fUBKEpCjhWogT8nXzZNuritp2ZS0hsUDLPA11W3F8ePGho3PdlvTDIuxCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bece25af9188b44a1d398e0c86937d1464bbacb8e86272711525f7bd6ce51898","last_reissued_at":"2026-07-05T09:38:46.178986Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:46.178986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Humanoid Locomotion with Perceptive Internal Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jiangmiao Pang, Junfeng Long, Junli Ren, Moji Shi, Ping Luo, Tao Huang, Zirui Wang","submitted_at":"2024-11-21T18:21:59Z","abstract_excerpt":"In contrast to quadruped robots that can navigate diverse terrains using a \"blind\" policy, humanoid robots require accurate perception for stable locomotion due to their high degrees of freedom and inherently unstable morphology. However, incorporating perceptual signals often introduces additional disturbances to the system, potentially reducing its robustness, generalizability, and efficiency. This paper presents the Perceptive Internal Model (PIM), which relies on onboard, continuously updated elevation maps centered around the robot to perceive its surroundings. We train the policy using g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14386","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/2411.14386/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":"2411.14386","created_at":"2026-07-05T09:38:46.179058+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14386v1","created_at":"2026-07-05T09:38:46.179058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14386","created_at":"2026-07-05T09:38:46.179058+00:00"},{"alias_kind":"pith_short_12","alias_value":"X3HCLL4RRC2E","created_at":"2026-07-05T09:38:46.179058+00:00"},{"alias_kind":"pith_short_16","alias_value":"X3HCLL4RRC2EUHJZ","created_at":"2026-07-05T09:38:46.179058+00:00"},{"alias_kind":"pith_short_8","alias_value":"X3HCLL4R","created_at":"2026-07-05T09:38:46.179058+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09286","citing_title":"VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20645","citing_title":"TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05873","citing_title":"LadderMan: Learning Humanoid Perceptive Ladder Climbing","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2505.18780","citing_title":"DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2510.25241","citing_title":"One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2511.06371","citing_title":"Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2602.06382","citing_title":"Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2602.09628","citing_title":"TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2602.11758","citing_title":"HAIC: Humanoid Agile Object Interaction Control via Dynamics-Aware World Model","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03846","citing_title":"SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR","json":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR.json","graph_json":"https://pith.science/api/pith-number/X3HCLL4RRC2EUHJZRYGINE35CR/graph.json","events_json":"https://pith.science/api/pith-number/X3HCLL4RRC2EUHJZRYGINE35CR/events.json","paper":"https://pith.science/paper/X3HCLL4R"},"agent_actions":{"view_html":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR","download_json":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR.json","view_paper":"https://pith.science/paper/X3HCLL4R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14386&json=true","fetch_graph":"https://pith.science/api/pith-number/X3HCLL4RRC2EUHJZRYGINE35CR/graph.json","fetch_events":"https://pith.science/api/pith-number/X3HCLL4RRC2EUHJZRYGINE35CR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR/action/storage_attestation","attest_author":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR/action/author_attestation","sign_citation":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR/action/citation_signature","submit_replication":"https://pith.science/pith/X3HCLL4RRC2EUHJZRYGINE35CR/action/replication_record"}},"created_at":"2026-07-05T09:38:46.179058+00:00","updated_at":"2026-07-05T09:38:46.179058+00:00"}