{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RG5UNAH6HKOQGC3PDPEKZ6SUFV","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":"e66818d2231d172502eb365adbeb49ffed4b64b944298509caa012b535a18110","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-12T18:11:12Z","title_canon_sha256":"c9acf5b2999973ca8890f38e6f2c2ce2861789aac27e493118eba82c39b5c6f2"},"schema_version":"1.0","source":{"id":"2605.12628","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12628","created_at":"2026-05-18T03:10:00Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12628v1","created_at":"2026-05-18T03:10:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12628","created_at":"2026-05-18T03:10:00Z"},{"alias_kind":"pith_short_12","alias_value":"RG5UNAH6HKOQ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"RG5UNAH6HKOQGC3P","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"RG5UNAH6","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:187ddbaf22b5d6c0e4a55aac0558d70b5a293309e065a75ff3fd161b5805bd07","target":"graph","created_at":"2026-05-18T03:10:00Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Our planning architecture is able to naturally regulate the speed of the vehicle based on the environment and consistently demonstrates intelligent behavior over miles of diverse terrain."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That deviations from the proposed structure in learning distributional dynamics materially degrade MPC performance, and that the learned model generalizes beyond the specific off-road dataset without introducing hidden conservatism or instability."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A structured learning approach for multistep distributional dynamics in belief space enables real-time risk-aware MPC, validated via ablation on real off-road data and deployment on a full-sized vehicle."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A structured multistep approach to learning distributional dynamics enables risk-aware MPC that naturally regulates speed in off-road driving."}],"snapshot_sha256":"ba8df5783045ad00fe8dd4462f8a4e9c20d92171635071c0b7130b26ab405036"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"0b57bfbc2b410eb5c6342cb5e5da5e64658a98ee2916b514d2d16a6c96782c76"},"paper":{"abstract_excerpt":"As autonomous vehicles move from a simplified research setting to practical use, there exists a large gap between the dynamic behavior of a human driving and an autonomous system. Risk-aware behavior needs to naturally develop in order to scale to the demands of the real world. A major issue for risk-aware planning and control has been predicting how dynamical uncertainty evolves through time and optimizing plans that account for this without being overly conservative. Here, we present a learning framework to predict distributional dynamics that can be optimized in real time for Model Predicti","authors_text":"Bogdan Vlahov, Evangelos A. Theodorou, Jason Gibson, Patrick Spieler","cross_cats":[],"headline":"A structured multistep approach to learning distributional dynamics enables risk-aware MPC that naturally regulates speed in off-road driving.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-12T18:11:12Z","title":"Multistep Belief Space Dynamics Learning For Risk-Aware Control"},"references":{"count":58,"internal_anchors":1,"resolved_work":58,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"A comprehensive review on autonomous navigation,","work_id":"d1cd195f-96ec-4d7b-a2e4-9b8e536487b1","year":null},{"cited_arxiv_id":"","doi":"10.1145/3727642","is_internal_anchor":false,"ref_index":2,"title":"Available: https://doi.org/10.1145/3727642 1","work_id":"3dd8fd14-71a6-49c3-aa7d-17d5c300fc15","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Parting with misconceptions about learning- based vehicle motion planning","work_id":"ca47adee-d361-4755-9f62-251c8a668963","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Quantifying generalization in reinforcement learning,","work_id":"76801677-183d-4fa1-a3b2-27ab614f9998","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"A survey on unmanned surface vehicles for disaster robotics: Main challenges and directions,","work_id":"f3384bcc-ada6-4b5b-bfa9-bebfdc5f980e","year":2019}],"snapshot_sha256":"2291ca454b9319c8a4ae9bee06d5bd3370bd68c62750980a98b4bf7d07a6734c"},"source":{"id":"2605.12628","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T20:41:08.745430Z","id":"4807b1c3-d863-442f-9958-233d7edc1d6d","model_set":{"reader":"grok-4.3"},"one_line_summary":"A structured learning approach for multistep distributional dynamics in belief space enables real-time risk-aware MPC, validated via ablation on real off-road data and deployment on a full-sized vehicle.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A structured multistep approach to learning distributional dynamics enables risk-aware MPC that naturally regulates speed in off-road driving.","strongest_claim":"Our planning architecture is able to naturally regulate the speed of the vehicle based on the environment and consistently demonstrates intelligent behavior over miles of diverse terrain.","weakest_assumption":"That deviations from the proposed structure in learning distributional dynamics materially degrade MPC performance, and that the learned model generalizes beyond the specific off-road dataset without introducing hidden conservatism or instability."}},"verdict_id":"4807b1c3-d863-442f-9958-233d7edc1d6d"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5d2de451fe657637c4951ef3861a7c1b0250d289b127f735e729748ba2f418de","target":"record","created_at":"2026-05-18T03:10:00Z","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":"e66818d2231d172502eb365adbeb49ffed4b64b944298509caa012b535a18110","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-12T18:11:12Z","title_canon_sha256":"c9acf5b2999973ca8890f38e6f2c2ce2861789aac27e493118eba82c39b5c6f2"},"schema_version":"1.0","source":{"id":"2605.12628","kind":"arxiv","version":1}},"canonical_sha256":"89bb4680fe3a9d030b6f1bc8acfa542d7e2fc991d2b493a9d6d704dd90eea31d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89bb4680fe3a9d030b6f1bc8acfa542d7e2fc991d2b493a9d6d704dd90eea31d","first_computed_at":"2026-05-18T03:10:00.244299Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:10:00.244299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2QKCSMunoBKrXTnPmjAe8IVlweSC4g9LgYkRECbPEelIg7M7/WF2ah4HfnyTZPZwy7MRxIwz7cm6q326Wh/6Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:10:00.244750Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12628","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d2de451fe657637c4951ef3861a7c1b0250d289b127f735e729748ba2f418de","sha256:187ddbaf22b5d6c0e4a55aac0558d70b5a293309e065a75ff3fd161b5805bd07"],"state_sha256":"6b930eb387cbae4a513244fa9bdfff8ecca0d352ff2a4b3b139ae85c91a78b1a"}