{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:V55GPF46JNQO6JKK3XGKHEPIR6","short_pith_number":"pith:V55GPF46","schema_version":"1.0","canonical_sha256":"af7a67979e4b60ef254addcca391e88fb774c4190c79b77ec80de76331a3b8a8","source":{"kind":"arxiv","id":"2208.00322","version":1},"attestation_state":"computed","paper":{"title":"PrePARE: Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ali-akbar Agha-mohammadi, Anushri Dixit, Arndt F. Schilling, David Fan, Kyohei Otsu, Robin Schmid, Sharmita Dey, Thomas Touma","submitted_at":"2022-07-30T23:37:31Z","abstract_excerpt":"Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be resolved by adjusting the robot's locomotion by switching to more conservative and stable locomotion modes, such as crawl mode (where three feet are in contact with the ground always) or amble mode (where one foot touches down at a time) to prevent potential falls. To tackle these challenges, we propose an approach to learn a model from past robot experience for"},"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":"2208.00322","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2022-07-30T23:37:31Z","cross_cats_sorted":[],"title_canon_sha256":"45402dcdf243da41b8acd88c0350725f49d44da453f4d54ea5afd9b11f4c00ab","abstract_canon_sha256":"1bf8dceccb1087c11debd81d7326ffede5356c2240dfc3bbda930319c03e0618"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:44:50.142091Z","signature_b64":"q/TnMUS5w3VhN3UySbaBHDqY88yBaunVEWwvwH+opU5QN0pQvUytXpWFBErUaFv7Ecxz2Wv0LVF57zBXOy0SDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af7a67979e4b60ef254addcca391e88fb774c4190c79b77ec80de76331a3b8a8","last_reissued_at":"2026-07-05T04:44:50.141694Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:44:50.141694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PrePARE: Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ali-akbar Agha-mohammadi, Anushri Dixit, Arndt F. Schilling, David Fan, Kyohei Otsu, Robin Schmid, Sharmita Dey, Thomas Touma","submitted_at":"2022-07-30T23:37:31Z","abstract_excerpt":"Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be resolved by adjusting the robot's locomotion by switching to more conservative and stable locomotion modes, such as crawl mode (where three feet are in contact with the ground always) or amble mode (where one foot touches down at a time) to prevent potential falls. To tackle these challenges, we propose an approach to learn a model from past robot experience for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00322","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/2208.00322/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":"2208.00322","created_at":"2026-07-05T04:44:50.141756+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.00322v1","created_at":"2026-07-05T04:44:50.141756+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00322","created_at":"2026-07-05T04:44:50.141756+00:00"},{"alias_kind":"pith_short_12","alias_value":"V55GPF46JNQO","created_at":"2026-07-05T04:44:50.141756+00:00"},{"alias_kind":"pith_short_16","alias_value":"V55GPF46JNQO6JKK","created_at":"2026-07-05T04:44:50.141756+00:00"},{"alias_kind":"pith_short_8","alias_value":"V55GPF46","created_at":"2026-07-05T04:44:50.141756+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.13461","citing_title":"An Addendum to NeBula: Towards Extending TEAM CoSTAR's Solution to Larger Scale Environments","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6","json":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6.json","graph_json":"https://pith.science/api/pith-number/V55GPF46JNQO6JKK3XGKHEPIR6/graph.json","events_json":"https://pith.science/api/pith-number/V55GPF46JNQO6JKK3XGKHEPIR6/events.json","paper":"https://pith.science/paper/V55GPF46"},"agent_actions":{"view_html":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6","download_json":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6.json","view_paper":"https://pith.science/paper/V55GPF46","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.00322&json=true","fetch_graph":"https://pith.science/api/pith-number/V55GPF46JNQO6JKK3XGKHEPIR6/graph.json","fetch_events":"https://pith.science/api/pith-number/V55GPF46JNQO6JKK3XGKHEPIR6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6/action/storage_attestation","attest_author":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6/action/author_attestation","sign_citation":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6/action/citation_signature","submit_replication":"https://pith.science/pith/V55GPF46JNQO6JKK3XGKHEPIR6/action/replication_record"}},"created_at":"2026-07-05T04:44:50.141756+00:00","updated_at":"2026-07-05T04:44:50.141756+00:00"}