{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5E7A54UKZ3CP7IWVBQVWNXK6Z6","short_pith_number":"pith:5E7A54UK","schema_version":"1.0","canonical_sha256":"e93e0ef28acec4ffa2d50c2b66dd5ecfaf6fce3da2c48c676b0cf04298e89346","source":{"kind":"arxiv","id":"2410.06493","version":1},"attestation_state":"computed","paper":{"title":"BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY","math.OC"],"primary_cat":"cs.RO","authors_text":"Kwangki Kim, Minchan Jung","submitted_at":"2024-10-09T02:36:35Z","abstract_excerpt":"This paper introduces the Bidirectional Clustered MPPI (BiC-MPPI) algorithm, a novel trajectory optimization method aimed at enhancing goal-directed guidance within the Model Predictive Path Integral (MPPI) framework. BiC-MPPI incorporates bidirectional dynamics approximations and a new guide cost mechanism, improving both trajectory planning and goal-reaching performance. By leveraging forward and backward rollouts, the bidirectional approach ensures effective trajectory connections between initial and terminal states, while the guide cost helps discover dynamically feasible paths. Experiment"},"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":"2410.06493","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-09T02:36:35Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY","math.OC"],"title_canon_sha256":"2cbde03ff0bd394029fd224e6b373889bf586adc980a7987a7e5073d4bc21cd0","abstract_canon_sha256":"e16166159c92ddb3266bfde4b18631ca3bb252ef6c8112d088c680962fecb704"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:18:02.547095Z","signature_b64":"kUiDt0j9hq/dqR3M2YS5xPvyvbfmKumzF9xRt3mXbXHHnLIbclGraTiJuZEpUweYZOBpf3fIFvy9B2ePAz+0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e93e0ef28acec4ffa2d50c2b66dd5ecfaf6fce3da2c48c676b0cf04298e89346","last_reissued_at":"2026-07-05T09:18:02.546749Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:18:02.546749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY","math.OC"],"primary_cat":"cs.RO","authors_text":"Kwangki Kim, Minchan Jung","submitted_at":"2024-10-09T02:36:35Z","abstract_excerpt":"This paper introduces the Bidirectional Clustered MPPI (BiC-MPPI) algorithm, a novel trajectory optimization method aimed at enhancing goal-directed guidance within the Model Predictive Path Integral (MPPI) framework. BiC-MPPI incorporates bidirectional dynamics approximations and a new guide cost mechanism, improving both trajectory planning and goal-reaching performance. By leveraging forward and backward rollouts, the bidirectional approach ensures effective trajectory connections between initial and terminal states, while the guide cost helps discover dynamically feasible paths. Experiment"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.06493","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/2410.06493/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":"2410.06493","created_at":"2026-07-05T09:18:02.546811+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.06493v1","created_at":"2026-07-05T09:18:02.546811+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.06493","created_at":"2026-07-05T09:18:02.546811+00:00"},{"alias_kind":"pith_short_12","alias_value":"5E7A54UKZ3CP","created_at":"2026-07-05T09:18:02.546811+00:00"},{"alias_kind":"pith_short_16","alias_value":"5E7A54UKZ3CP7IWV","created_at":"2026-07-05T09:18:02.546811+00:00"},{"alias_kind":"pith_short_8","alias_value":"5E7A54UK","created_at":"2026-07-05T09:18:02.546811+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.16386","citing_title":"CSC-MPPI: A Novel Constrained MPPI Framework with DBSCAN for Reliable Obstacle Avoidance","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6","json":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6.json","graph_json":"https://pith.science/api/pith-number/5E7A54UKZ3CP7IWVBQVWNXK6Z6/graph.json","events_json":"https://pith.science/api/pith-number/5E7A54UKZ3CP7IWVBQVWNXK6Z6/events.json","paper":"https://pith.science/paper/5E7A54UK"},"agent_actions":{"view_html":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6","download_json":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6.json","view_paper":"https://pith.science/paper/5E7A54UK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.06493&json=true","fetch_graph":"https://pith.science/api/pith-number/5E7A54UKZ3CP7IWVBQVWNXK6Z6/graph.json","fetch_events":"https://pith.science/api/pith-number/5E7A54UKZ3CP7IWVBQVWNXK6Z6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6/action/storage_attestation","attest_author":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6/action/author_attestation","sign_citation":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6/action/citation_signature","submit_replication":"https://pith.science/pith/5E7A54UKZ3CP7IWVBQVWNXK6Z6/action/replication_record"}},"created_at":"2026-07-05T09:18:02.546811+00:00","updated_at":"2026-07-05T09:18:02.546811+00:00"}