{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Q5HXEMYPNWWLS442ESECFL2E7Y","short_pith_number":"pith:Q5HXEMYP","schema_version":"1.0","canonical_sha256":"874f72330f6dacb9739a248822af44fe2da517df205de7dc35f3330eb949da69","source":{"kind":"arxiv","id":"2503.02048","version":1},"attestation_state":"computed","paper":{"title":"FRMD: Fast Robot Motion Diffusion with Consistency-Distilled Movement Primitives for Smooth Action Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Jun Jin, Xirui Shi","submitted_at":"2025-03-03T20:56:39Z","abstract_excerpt":"We consider the problem of using diffusion models to generate fast, smooth, and temporally consistent robot motions. Although diffusion models have demonstrated superior performance in robot learning due to their task scalability and multi-modal flexibility, they suffer from two fundamental limitations: (1) they often produce non-smooth, jerky motions due to their inability to capture temporally consistent movement dynamics, and (2) their iterative sampling process incurs prohibitive latency for many robotic tasks. Inspired by classic robot motion generation methods such as DMPs and ProMPs, wh"},"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":"2503.02048","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-03T20:56:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0110bacd1335637a51b735202d289af8156823209bda3ba272b8a8c4a9bde242","abstract_canon_sha256":"10927c427df73980d5c75136364fe5bee4b09aeb6fe26fccde22556052a4814e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:23:35.303573Z","signature_b64":"0I28alEZ9LPPFZ2nuPCh0ATNTZsQNHnvcYr3CwwOuCnhZo3cEP0JJyPXZdxpDksRxZLwhikiixUE5+4wl2sXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"874f72330f6dacb9739a248822af44fe2da517df205de7dc35f3330eb949da69","last_reissued_at":"2026-07-05T10:23:35.303072Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:23:35.303072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FRMD: Fast Robot Motion Diffusion with Consistency-Distilled Movement Primitives for Smooth Action Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Jun Jin, Xirui Shi","submitted_at":"2025-03-03T20:56:39Z","abstract_excerpt":"We consider the problem of using diffusion models to generate fast, smooth, and temporally consistent robot motions. Although diffusion models have demonstrated superior performance in robot learning due to their task scalability and multi-modal flexibility, they suffer from two fundamental limitations: (1) they often produce non-smooth, jerky motions due to their inability to capture temporally consistent movement dynamics, and (2) their iterative sampling process incurs prohibitive latency for many robotic tasks. Inspired by classic robot motion generation methods such as DMPs and ProMPs, wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02048","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/2503.02048/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":"2503.02048","created_at":"2026-07-05T10:23:35.303131+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02048v1","created_at":"2026-07-05T10:23:35.303131+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02048","created_at":"2026-07-05T10:23:35.303131+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q5HXEMYPNWWL","created_at":"2026-07-05T10:23:35.303131+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q5HXEMYPNWWLS442","created_at":"2026-07-05T10:23:35.303131+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q5HXEMYP","created_at":"2026-07-05T10:23:35.303131+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20135","citing_title":"Frequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31321","citing_title":"Surface Constraint Policy for Learning Surface-Constrained and Dynamically Feasible Robot Skills","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y","json":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y.json","graph_json":"https://pith.science/api/pith-number/Q5HXEMYPNWWLS442ESECFL2E7Y/graph.json","events_json":"https://pith.science/api/pith-number/Q5HXEMYPNWWLS442ESECFL2E7Y/events.json","paper":"https://pith.science/paper/Q5HXEMYP"},"agent_actions":{"view_html":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y","download_json":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y.json","view_paper":"https://pith.science/paper/Q5HXEMYP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02048&json=true","fetch_graph":"https://pith.science/api/pith-number/Q5HXEMYPNWWLS442ESECFL2E7Y/graph.json","fetch_events":"https://pith.science/api/pith-number/Q5HXEMYPNWWLS442ESECFL2E7Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y/action/storage_attestation","attest_author":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y/action/author_attestation","sign_citation":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y/action/citation_signature","submit_replication":"https://pith.science/pith/Q5HXEMYPNWWLS442ESECFL2E7Y/action/replication_record"}},"created_at":"2026-07-05T10:23:35.303131+00:00","updated_at":"2026-07-05T10:23:35.303131+00:00"}