{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZRM4CFQD465HC6QOYFCXIBGEHH","short_pith_number":"pith:ZRM4CFQD","schema_version":"1.0","canonical_sha256":"cc59c11603e7ba717a0ec1457404c439df8b7f81b0356acd3e68e4e2af656e20","source":{"kind":"arxiv","id":"2404.14405","version":2},"attestation_state":"computed","paper":{"title":"Learning H-Infinity Locomotion Control","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Dahua Lin, Jiangmiao Pang, Junfeng Long, Quanyi Li, Wenye Yu, Zirui Wang","submitted_at":"2024-04-22T17:59:07Z","abstract_excerpt":"Stable locomotion in precipitous environments is an essential task for quadruped robots, requiring the ability to resist various external disturbances. Recent neural policies enhance robustness against disturbances by learning to resist external forces sampled from a fixed distribution in the simulated environment. However, the force generation process doesn't consider the robot's current state, making it difficult to identify the most effective direction and magnitude that can push the robot to the most unstable but recoverable state. Thus, challenging cases in the buffer are insufficient to "},"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":"2404.14405","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2024-04-22T17:59:07Z","cross_cats_sorted":[],"title_canon_sha256":"4843ea6e82e27da4c0e80bbf8cf23a39e75872bf68b9f0c928f68d7b2ca99987","abstract_canon_sha256":"10ecf4b9cc0d1a58497ebd0983e0b97bb849e7a908e7ba82dd660a93ddf2f2ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:30:51.719095Z","signature_b64":"rTlUKIWN0n6VxsSpJ7vZowdVfF6r3sGkIgbxwl0phINq6esZV03gu1Ps/j1GFuOHkuTTHzxs+ixXUfAfpKdxAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc59c11603e7ba717a0ec1457404c439df8b7f81b0356acd3e68e4e2af656e20","last_reissued_at":"2026-07-05T08:30:51.718557Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:30:51.718557Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning H-Infinity Locomotion Control","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Dahua Lin, Jiangmiao Pang, Junfeng Long, Quanyi Li, Wenye Yu, Zirui Wang","submitted_at":"2024-04-22T17:59:07Z","abstract_excerpt":"Stable locomotion in precipitous environments is an essential task for quadruped robots, requiring the ability to resist various external disturbances. Recent neural policies enhance robustness against disturbances by learning to resist external forces sampled from a fixed distribution in the simulated environment. However, the force generation process doesn't consider the robot's current state, making it difficult to identify the most effective direction and magnitude that can push the robot to the most unstable but recoverable state. Thus, challenging cases in the buffer are insufficient to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.14405","kind":"arxiv","version":2},"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/2404.14405/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":"2404.14405","created_at":"2026-07-05T08:30:51.718630+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.14405v2","created_at":"2026-07-05T08:30:51.718630+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.14405","created_at":"2026-07-05T08:30:51.718630+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZRM4CFQD465H","created_at":"2026-07-05T08:30:51.718630+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZRM4CFQD465HC6QO","created_at":"2026-07-05T08:30:51.718630+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZRM4CFQD","created_at":"2026-07-05T08:30:51.718630+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.03846","citing_title":"SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH","json":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH.json","graph_json":"https://pith.science/api/pith-number/ZRM4CFQD465HC6QOYFCXIBGEHH/graph.json","events_json":"https://pith.science/api/pith-number/ZRM4CFQD465HC6QOYFCXIBGEHH/events.json","paper":"https://pith.science/paper/ZRM4CFQD"},"agent_actions":{"view_html":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH","download_json":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH.json","view_paper":"https://pith.science/paper/ZRM4CFQD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.14405&json=true","fetch_graph":"https://pith.science/api/pith-number/ZRM4CFQD465HC6QOYFCXIBGEHH/graph.json","fetch_events":"https://pith.science/api/pith-number/ZRM4CFQD465HC6QOYFCXIBGEHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH/action/storage_attestation","attest_author":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH/action/author_attestation","sign_citation":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH/action/citation_signature","submit_replication":"https://pith.science/pith/ZRM4CFQD465HC6QOYFCXIBGEHH/action/replication_record"}},"created_at":"2026-07-05T08:30:51.718630+00:00","updated_at":"2026-07-05T08:30:51.718630+00:00"}