{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CIFZAZ3RG7BJJNO3EF3I6BCNZY","short_pith_number":"pith:CIFZAZ3R","schema_version":"1.0","canonical_sha256":"120b90677137c294b5db21768f044dce0b210b1d261c60481cd390203e58f97d","source":{"kind":"arxiv","id":"2410.16727","version":1},"attestation_state":"computed","paper":{"title":"DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Adithyavairavan Murali, Arsalan Mousavian, Balakumar Sundaralingam, Dieter Fox, Huang Huang, Ken Goldberg","submitted_at":"2024-10-22T06:25:34Z","abstract_excerpt":"Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all seeds could get stuck in local minima. One such setting is collision-free motion optimization for robot manipulation, where optimization converges quickly on easy problems but struggle in obstacle dense environments (e.g., a cluttered cabinet or table). In these situations, graph-based planning algorithms are used to obtain seeds, resulting in significant slowdowns. We propose DiffusionSeeder, a diffusion based appr"},"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":"2410.16727","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-10-22T06:25:34Z","cross_cats_sorted":[],"title_canon_sha256":"d310f5c081140164ddce83e60a43043931afed36b023bf7d2a94d2e8d3b17cae","abstract_canon_sha256":"e8d90c89cb97f8c3e8180a942f08fe6a217ebb6277848b21fb6e0c7b7bdcb2c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:24:05.757885Z","signature_b64":"4EcyFNN/YUHvdEYWjV8MJRuHVFaei20TU8cE9gm/Ixi3t5xGqWtGoC8CHDu0Vkrx9LXo89Q38sQCNEmz9QKuCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"120b90677137c294b5db21768f044dce0b210b1d261c60481cd390203e58f97d","last_reissued_at":"2026-07-05T09:24:05.757361Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:24:05.757361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Adithyavairavan Murali, Arsalan Mousavian, Balakumar Sundaralingam, Dieter Fox, Huang Huang, Ken Goldberg","submitted_at":"2024-10-22T06:25:34Z","abstract_excerpt":"Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all seeds could get stuck in local minima. One such setting is collision-free motion optimization for robot manipulation, where optimization converges quickly on easy problems but struggle in obstacle dense environments (e.g., a cluttered cabinet or table). In these situations, graph-based planning algorithms are used to obtain seeds, resulting in significant slowdowns. We propose DiffusionSeeder, a diffusion based appr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.16727","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/2410.16727/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":"2410.16727","created_at":"2026-07-05T09:24:05.757432+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.16727v1","created_at":"2026-07-05T09:24:05.757432+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.16727","created_at":"2026-07-05T09:24:05.757432+00:00"},{"alias_kind":"pith_short_12","alias_value":"CIFZAZ3RG7BJ","created_at":"2026-07-05T09:24:05.757432+00:00"},{"alias_kind":"pith_short_16","alias_value":"CIFZAZ3RG7BJJNO3","created_at":"2026-07-05T09:24:05.757432+00:00"},{"alias_kind":"pith_short_8","alias_value":"CIFZAZ3R","created_at":"2026-07-05T09:24:05.757432+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.24690","citing_title":"Sum of Costs Diffusion with Dynamic Guidance for Motion Planning","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY","json":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY.json","graph_json":"https://pith.science/api/pith-number/CIFZAZ3RG7BJJNO3EF3I6BCNZY/graph.json","events_json":"https://pith.science/api/pith-number/CIFZAZ3RG7BJJNO3EF3I6BCNZY/events.json","paper":"https://pith.science/paper/CIFZAZ3R"},"agent_actions":{"view_html":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY","download_json":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY.json","view_paper":"https://pith.science/paper/CIFZAZ3R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.16727&json=true","fetch_graph":"https://pith.science/api/pith-number/CIFZAZ3RG7BJJNO3EF3I6BCNZY/graph.json","fetch_events":"https://pith.science/api/pith-number/CIFZAZ3RG7BJJNO3EF3I6BCNZY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY/action/storage_attestation","attest_author":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY/action/author_attestation","sign_citation":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY/action/citation_signature","submit_replication":"https://pith.science/pith/CIFZAZ3RG7BJJNO3EF3I6BCNZY/action/replication_record"}},"created_at":"2026-07-05T09:24:05.757432+00:00","updated_at":"2026-07-05T09:24:05.757432+00:00"}