{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QWMQNSCANZGCWALKYPT6EG53OR","short_pith_number":"pith:QWMQNSCA","canonical_record":{"source":{"id":"2506.09859","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-06-11T15:31:25Z","cross_cats_sorted":[],"title_canon_sha256":"c2394e758e1d4adab202906fb6e5267db77eea6966fb09bf0fbd640319c1169a","abstract_canon_sha256":"31a2089ced456624dc12cf88f0ff50c61441df29b05d1bef9d4bf55173808a4a"},"schema_version":"1.0"},"canonical_sha256":"859906c8406e4c2b016ac3e7e21bbb7473d4b282d1f4e7e6f0b40152988e1161","source":{"kind":"arxiv","id":"2506.09859","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09859","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09859v2","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09859","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"pith_short_12","alias_value":"QWMQNSCANZGC","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"pith_short_16","alias_value":"QWMQNSCANZGCWALK","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"pith_short_8","alias_value":"QWMQNSCA","created_at":"2026-07-05T11:41:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QWMQNSCANZGCWALKYPT6EG53OR","target":"record","payload":{"canonical_record":{"source":{"id":"2506.09859","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-06-11T15:31:25Z","cross_cats_sorted":[],"title_canon_sha256":"c2394e758e1d4adab202906fb6e5267db77eea6966fb09bf0fbd640319c1169a","abstract_canon_sha256":"31a2089ced456624dc12cf88f0ff50c61441df29b05d1bef9d4bf55173808a4a"},"schema_version":"1.0"},"canonical_sha256":"859906c8406e4c2b016ac3e7e21bbb7473d4b282d1f4e7e6f0b40152988e1161","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:41:43.068559Z","signature_b64":"yy05TmbH/EryVvmvMSraLZvGBGAd2JrAX6HPnfam4nGNuXbKOvThKSM67HEr4ARKQj9vKNxUJ3uR866GO4WBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"859906c8406e4c2b016ac3e7e21bbb7473d4b282d1f4e7e6f0b40152988e1161","last_reissued_at":"2026-07-05T11:41:43.068085Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:41:43.068085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.09859","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:41:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qNWDoBxFr9w4sa5+E8u6tbaqI9K6IfqvfVJXNm2Vb77Z5CCv64nQyFk5Lgv9QZ1BjPwTmfZminPxbpjXPcXIBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T05:34:39.579011Z"},"content_sha256":"dc291bfccc4cc2e35cc97a2782de3e7390fcd1b0a30692dcd34f1aec23772890","schema_version":"1.0","event_id":"sha256:dc291bfccc4cc2e35cc97a2782de3e7390fcd1b0a30692dcd34f1aec23772890"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QWMQNSCANZGCWALKYPT6EG53OR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical Learning-Enhanced MPC for Safe Crowd Navigation with Heterogeneous Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chao Wang, Huajian Liu, Kunpeng Fan, Wei Dong, Yixuan Feng, Yongzhuo Gao","submitted_at":"2025-06-11T15:31:25Z","abstract_excerpt":"In this paper, we propose a novel hierarchical framework for robot navigation in dynamic environments with heterogeneous constraints. Our approach leverages a graph neural network trained via reinforcement learning (RL) to efficiently estimate the robot's cost-to-go, formulated as local goal recommendations. A spatio-temporal path-searching module, which accounts for kinematic constraints, is then employed to generate a reference trajectory to facilitate solving the non-convex optimization problem used for explicit constraint enforcement. More importantly, we introduce an incremental action-ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09859","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/2506.09859/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":"2026-07-05T11:41:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dxynkfV613uzerF5KFz9xiy1ud4XH89gfqMHfav5D0klT9S5/hjZnGB8pK3XjC4R9GtQSnedfo3NmMo4uIlOAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T05:34:39.579506Z"},"content_sha256":"1904e66a72f74b129b4f8e035519133dc6d5847b1ea6f8fecd446c2fb8a27cad","schema_version":"1.0","event_id":"sha256:1904e66a72f74b129b4f8e035519133dc6d5847b1ea6f8fecd446c2fb8a27cad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QWMQNSCANZGCWALKYPT6EG53OR/bundle.json","state_url":"https://pith.science/pith/QWMQNSCANZGCWALKYPT6EG53OR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QWMQNSCANZGCWALKYPT6EG53OR/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":"2026-08-04T05:34:39Z","links":{"resolver":"https://pith.science/pith/QWMQNSCANZGCWALKYPT6EG53OR","bundle":"https://pith.science/pith/QWMQNSCANZGCWALKYPT6EG53OR/bundle.json","state":"https://pith.science/pith/QWMQNSCANZGCWALKYPT6EG53OR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QWMQNSCANZGCWALKYPT6EG53OR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QWMQNSCANZGCWALKYPT6EG53OR","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":"31a2089ced456624dc12cf88f0ff50c61441df29b05d1bef9d4bf55173808a4a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-06-11T15:31:25Z","title_canon_sha256":"c2394e758e1d4adab202906fb6e5267db77eea6966fb09bf0fbd640319c1169a"},"schema_version":"1.0","source":{"id":"2506.09859","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09859","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09859v2","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09859","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"pith_short_12","alias_value":"QWMQNSCANZGC","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"pith_short_16","alias_value":"QWMQNSCANZGCWALK","created_at":"2026-07-05T11:41:43Z"},{"alias_kind":"pith_short_8","alias_value":"QWMQNSCA","created_at":"2026-07-05T11:41:43Z"}],"graph_snapshots":[{"event_id":"sha256:1904e66a72f74b129b4f8e035519133dc6d5847b1ea6f8fecd446c2fb8a27cad","target":"graph","created_at":"2026-07-05T11:41:43Z","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/2506.09859/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a novel hierarchical framework for robot navigation in dynamic environments with heterogeneous constraints. Our approach leverages a graph neural network trained via reinforcement learning (RL) to efficiently estimate the robot's cost-to-go, formulated as local goal recommendations. A spatio-temporal path-searching module, which accounts for kinematic constraints, is then employed to generate a reference trajectory to facilitate solving the non-convex optimization problem used for explicit constraint enforcement. More importantly, we introduce an incremental action-ma","authors_text":"Chao Wang, Huajian Liu, Kunpeng Fan, Wei Dong, Yixuan Feng, Yongzhuo Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-06-11T15:31:25Z","title":"Hierarchical Learning-Enhanced MPC for Safe Crowd Navigation with Heterogeneous Constraints"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09859","kind":"arxiv","version":2},"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:dc291bfccc4cc2e35cc97a2782de3e7390fcd1b0a30692dcd34f1aec23772890","target":"record","created_at":"2026-07-05T11:41:43Z","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":"31a2089ced456624dc12cf88f0ff50c61441df29b05d1bef9d4bf55173808a4a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-06-11T15:31:25Z","title_canon_sha256":"c2394e758e1d4adab202906fb6e5267db77eea6966fb09bf0fbd640319c1169a"},"schema_version":"1.0","source":{"id":"2506.09859","kind":"arxiv","version":2}},"canonical_sha256":"859906c8406e4c2b016ac3e7e21bbb7473d4b282d1f4e7e6f0b40152988e1161","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"859906c8406e4c2b016ac3e7e21bbb7473d4b282d1f4e7e6f0b40152988e1161","first_computed_at":"2026-07-05T11:41:43.068085Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:41:43.068085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yy05TmbH/EryVvmvMSraLZvGBGAd2JrAX6HPnfam4nGNuXbKOvThKSM67HEr4ARKQj9vKNxUJ3uR866GO4WBCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:41:43.068559Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.09859","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dc291bfccc4cc2e35cc97a2782de3e7390fcd1b0a30692dcd34f1aec23772890","sha256:1904e66a72f74b129b4f8e035519133dc6d5847b1ea6f8fecd446c2fb8a27cad"],"state_sha256":"298eacd6947bbeaa373c686b027bc218f60cea1eb65424c7a32402140eb32777"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9zJXEOobNEFr6hk1O4b4y1qtoMNF63md3y5EXa4qXxxrQGd77jczH3dOV3X1f9qSQJ2TND7Sk/NE+r5gr+NpCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T05:34:39.583249Z","bundle_sha256":"24d5a80d2782a6e5a49feb35626062c66ebb35bb7d71d22805889c19f059cce0"}}