{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4PAWLNYFRN3ZDIQRWXQFXDTQV4","short_pith_number":"pith:4PAWLNYF","schema_version":"1.0","canonical_sha256":"e3c165b7058b7791a211b5e05b8e70af17a651d25a948d058ef516f50230164c","source":{"kind":"arxiv","id":"2508.11129","version":1},"attestation_state":"computed","paper":{"title":"Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Aaron D. Ames, Blake Werner, Gilbert Bahati, Lizhi Yang, Ryan K. Cosner, Ryan M. Bena","submitted_at":"2025-08-15T00:35:27Z","abstract_excerpt":"Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular, legged robots typically possess manipulable asymmetric geometries which must be considered during safety-critical trajectory planning. This work proposes a predictive safety filter: a nonlinear model predictive control (MPC) algorithm for online trajectory generation with geometry-aware safety constraints based on control barrier functions (CBFs). Critically, our method leverages Poisson safety functions to numerical"},"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":"2508.11129","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-08-15T00:35:27Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"c83c3bad55d187391fc9b8a7c083bd157b137ac4d740f40a57f9e4f0330c7d99","abstract_canon_sha256":"5995c9934432a489b3919a0415c0d78f99a41345191c315053bce3b52eb261da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:19.048202Z","signature_b64":"ynhpfiI1T6PJtlXlHodbmssdwFu7Raqo6Y3/xUtcEEUD7BM3FuaMwr3YfK7LGJXZQsJy8Gv67TeLt+KY3HhyCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3c165b7058b7791a211b5e05b8e70af17a651d25a948d058ef516f50230164c","last_reissued_at":"2026-07-05T11:54:19.047755Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:19.047755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Aaron D. Ames, Blake Werner, Gilbert Bahati, Lizhi Yang, Ryan K. Cosner, Ryan M. Bena","submitted_at":"2025-08-15T00:35:27Z","abstract_excerpt":"Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular, legged robots typically possess manipulable asymmetric geometries which must be considered during safety-critical trajectory planning. This work proposes a predictive safety filter: a nonlinear model predictive control (MPC) algorithm for online trajectory generation with geometry-aware safety constraints based on control barrier functions (CBFs). Critically, our method leverages Poisson safety functions to numerical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.11129","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/2508.11129/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":"2508.11129","created_at":"2026-07-05T11:54:19.047809+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.11129v1","created_at":"2026-07-05T11:54:19.047809+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.11129","created_at":"2026-07-05T11:54:19.047809+00:00"},{"alias_kind":"pith_short_12","alias_value":"4PAWLNYFRN3Z","created_at":"2026-07-05T11:54:19.047809+00:00"},{"alias_kind":"pith_short_16","alias_value":"4PAWLNYFRN3ZDIQR","created_at":"2026-07-05T11:54:19.047809+00:00"},{"alias_kind":"pith_short_8","alias_value":"4PAWLNYF","created_at":"2026-07-05T11:54:19.047809+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/4PAWLNYFRN3ZDIQRWXQFXDTQV4","json":"https://pith.science/pith/4PAWLNYFRN3ZDIQRWXQFXDTQV4.json","graph_json":"https://pith.science/api/pith-number/4PAWLNYFRN3ZDIQRWXQFXDTQV4/graph.json","events_json":"https://pith.science/api/pith-number/4PAWLNYFRN3ZDIQRWXQFXDTQV4/events.json","paper":"https://pith.science/paper/4PAWLNYF"},"agent_actions":{"view_html":"https://pith.science/pith/4PAWLNYFRN3ZDIQRWXQFXDTQV4","download_json":"https://pith.science/pith/4PAWLNYFRN3ZDIQRWXQFXDTQV4.json","view_paper":"https://pith.science/paper/4PAWLNYF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.11129&json=true","fetch_graph":"https://pith.science/api/pith-number/4PAWLNYFRN3ZDIQRWXQFXDTQV4/graph.json","fetch_events":"https://pith.science/api/pith-number/4PAWLNYFRN3ZDIQRWXQFXDTQV4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4PAWLNYFRN3ZDIQRWXQFXDTQV4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4PAWLNYFRN3ZDIQRWXQFXDTQV4/action/storage_attestation","attest_author":"https://pith.science/pith/4PAWLNYFRN3ZDIQRWXQFXDTQV4/action/author_attestation","sign_citation":"https://pith.science/pith/4PAWLNYFRN3ZDIQRWXQFXDTQV4/action/citation_signature","submit_replication":"https://pith.science/pith/4PAWLNYFRN3ZDIQRWXQFXDTQV4/action/replication_record"}},"created_at":"2026-07-05T11:54:19.047809+00:00","updated_at":"2026-07-05T11:54:19.047809+00:00"}