{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IW4NSPFMD5PHERCWQ3D3NU3BZV","short_pith_number":"pith:IW4NSPFM","canonical_record":{"source":{"id":"2410.03066","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-04T01:15:15Z","cross_cats_sorted":[],"title_canon_sha256":"fa1a4f554ee47cdeb10d878419a969b82575090a3c48b662cc0db4deccf36339","abstract_canon_sha256":"7c4525ad4912114222923f59eb5435ad79027352fb1ebcbec7e660933278eaf4"},"schema_version":"1.0"},"canonical_sha256":"45b8d93cac1f5e72445686c7b6d361cd6a7f5e822d01184c09dfc9e348baa1af","source":{"kind":"arxiv","id":"2410.03066","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.03066","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"arxiv_version","alias_value":"2410.03066v1","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03066","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"pith_short_12","alias_value":"IW4NSPFMD5PH","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"pith_short_16","alias_value":"IW4NSPFMD5PHERCW","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"pith_short_8","alias_value":"IW4NSPFM","created_at":"2026-07-05T09:15:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IW4NSPFMD5PHERCWQ3D3NU3BZV","target":"record","payload":{"canonical_record":{"source":{"id":"2410.03066","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-04T01:15:15Z","cross_cats_sorted":[],"title_canon_sha256":"fa1a4f554ee47cdeb10d878419a969b82575090a3c48b662cc0db4deccf36339","abstract_canon_sha256":"7c4525ad4912114222923f59eb5435ad79027352fb1ebcbec7e660933278eaf4"},"schema_version":"1.0"},"canonical_sha256":"45b8d93cac1f5e72445686c7b6d361cd6a7f5e822d01184c09dfc9e348baa1af","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:43.670512Z","signature_b64":"aDeF8XdQEFO5PW6/I3e9D84tALaTUGZBUUG2a8GyP0fNU1Gz0+xwQL+WWOMnThlu0dnXnXpeN+pFvUgKA39vBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45b8d93cac1f5e72445686c7b6d361cd6a7f5e822d01184c09dfc9e348baa1af","last_reissued_at":"2026-07-05T09:15:43.670054Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:43.670054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.03066","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:15:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bDO10fwydlxgvviOHi3F1Rc08rBr6BdKyKDPmsiYQahUw4rIMr3YhYRfgpSfl6yPO5VsqlQaRG0HVkojJ7ntDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:10:21.189518Z"},"content_sha256":"91377fa2604169c51b187f9773bda25670fd151277b5006b259052748856bf08","schema_version":"1.0","event_id":"sha256:91377fa2604169c51b187f9773bda25670fd151277b5006b259052748856bf08"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IW4NSPFMD5PHERCWQ3D3NU3BZV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hybrid Classical/RL Local Planner for Ground Robot Navigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ilija Had\\v{z}i\\'c, Jeongran Lee, Matthew Andrews, Vishnu D. Sharma","submitted_at":"2024-10-04T01:15:15Z","abstract_excerpt":"Local planning is an optimization process within a mobile robot navigation stack that searches for the best velocity vector, given the robot and environment state. Depending on how the optimization criteria and constraints are defined, some planners may be better than others in specific situations. We consider two conceptually different planners. The first planner explores the velocity space in real-time and has superior path-tracking and motion smoothness performance. The second planner was trained using reinforcement learning methods to produce the best velocity based on its training $\"$expe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03066","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/2410.03066/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:15:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dsiwoBcGAPLdNaGvcAgoazxdWTcpFwxcKpbbw9ZmkycOOPXFh3jqbnzrXdr7IOxxtslax6673Ce+N9+jY6E/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:10:21.190256Z"},"content_sha256":"f53fbb848beb3dc8eeebe885bc84d5a471c8237a06a2b422434a6d43d45520ab","schema_version":"1.0","event_id":"sha256:f53fbb848beb3dc8eeebe885bc84d5a471c8237a06a2b422434a6d43d45520ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IW4NSPFMD5PHERCWQ3D3NU3BZV/bundle.json","state_url":"https://pith.science/pith/IW4NSPFMD5PHERCWQ3D3NU3BZV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IW4NSPFMD5PHERCWQ3D3NU3BZV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T15:10:21Z","links":{"resolver":"https://pith.science/pith/IW4NSPFMD5PHERCWQ3D3NU3BZV","bundle":"https://pith.science/pith/IW4NSPFMD5PHERCWQ3D3NU3BZV/bundle.json","state":"https://pith.science/pith/IW4NSPFMD5PHERCWQ3D3NU3BZV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IW4NSPFMD5PHERCWQ3D3NU3BZV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IW4NSPFMD5PHERCWQ3D3NU3BZV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7c4525ad4912114222923f59eb5435ad79027352fb1ebcbec7e660933278eaf4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-04T01:15:15Z","title_canon_sha256":"fa1a4f554ee47cdeb10d878419a969b82575090a3c48b662cc0db4deccf36339"},"schema_version":"1.0","source":{"id":"2410.03066","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.03066","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"arxiv_version","alias_value":"2410.03066v1","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03066","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"pith_short_12","alias_value":"IW4NSPFMD5PH","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"pith_short_16","alias_value":"IW4NSPFMD5PHERCW","created_at":"2026-07-05T09:15:43Z"},{"alias_kind":"pith_short_8","alias_value":"IW4NSPFM","created_at":"2026-07-05T09:15:43Z"}],"graph_snapshots":[{"event_id":"sha256:f53fbb848beb3dc8eeebe885bc84d5a471c8237a06a2b422434a6d43d45520ab","target":"graph","created_at":"2026-07-05T09:15:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2410.03066/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Local planning is an optimization process within a mobile robot navigation stack that searches for the best velocity vector, given the robot and environment state. Depending on how the optimization criteria and constraints are defined, some planners may be better than others in specific situations. We consider two conceptually different planners. The first planner explores the velocity space in real-time and has superior path-tracking and motion smoothness performance. The second planner was trained using reinforcement learning methods to produce the best velocity based on its training $\"$expe","authors_text":"Ilija Had\\v{z}i\\'c, Jeongran Lee, Matthew Andrews, Vishnu D. Sharma","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-04T01:15:15Z","title":"Hybrid Classical/RL Local Planner for Ground Robot Navigation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03066","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:91377fa2604169c51b187f9773bda25670fd151277b5006b259052748856bf08","target":"record","created_at":"2026-07-05T09:15:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7c4525ad4912114222923f59eb5435ad79027352fb1ebcbec7e660933278eaf4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-04T01:15:15Z","title_canon_sha256":"fa1a4f554ee47cdeb10d878419a969b82575090a3c48b662cc0db4deccf36339"},"schema_version":"1.0","source":{"id":"2410.03066","kind":"arxiv","version":1}},"canonical_sha256":"45b8d93cac1f5e72445686c7b6d361cd6a7f5e822d01184c09dfc9e348baa1af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45b8d93cac1f5e72445686c7b6d361cd6a7f5e822d01184c09dfc9e348baa1af","first_computed_at":"2026-07-05T09:15:43.670054Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:15:43.670054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aDeF8XdQEFO5PW6/I3e9D84tALaTUGZBUUG2a8GyP0fNU1Gz0+xwQL+WWOMnThlu0dnXnXpeN+pFvUgKA39vBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:15:43.670512Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.03066","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:91377fa2604169c51b187f9773bda25670fd151277b5006b259052748856bf08","sha256:f53fbb848beb3dc8eeebe885bc84d5a471c8237a06a2b422434a6d43d45520ab"],"state_sha256":"4222f3bbd1af9910ac19f701579414afedc9215d92fd59c2df32a00bf5447e19"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XW/vj9cv2qRbO6YbdMVfwbmgr9AZftI56hgV0fIvWxzU/h/R6DBnh2oPqyM1iCU3zwNxiZZEOxzweV1kF823DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T15:10:21.220261Z","bundle_sha256":"063281441dc302b8dd49521b77377556ee4b7f222667c4b3ec95f49e71c00410"}}