{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QF544YO4BQY6SR5RK7MXQIYSIO","short_pith_number":"pith:QF544YO4","schema_version":"1.0","canonical_sha256":"817bce61dc0c31e947b157d978231243bd2498b6a8e041888e798efe8729a029","source":{"kind":"arxiv","id":"2405.04235","version":2},"attestation_state":"computed","paper":{"title":"LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe Diffusion-based Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Hao Luan, Harold Soh, Pranav Goyal, Zeyu Feng","submitted_at":"2024-05-07T11:54:22Z","abstract_excerpt":"Operating effectively in complex environments while complying with specified constraints is crucial for the safe and successful deployment of robots that interact with and operate around people. In this work, we focus on generating long-horizon trajectories that adhere to novel static and temporally-extended constraints/instructions at test time. We propose a data-driven diffusion-based framework, LTLDoG, that modifies the inference steps of the reverse process given an instruction specified using finite linear temporal logic ($\\text{LTL}_f$). LTLDoG leverages a satisfaction value function on "},"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":"2405.04235","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-05-07T11:54:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"547c4d08a1d8facb57d5f77d62be6ec615ee9e906bcc1bda61a125d91294d2db","abstract_canon_sha256":"f8920d1ec25fc8f96ddd9fa7811994d391a0618862dff1a82fa19f777fdb8ef4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:02.589252Z","signature_b64":"ddpC09bMpTs3KaGMDseiLmqW3n81eH4v8CTjSMgVtyZhfOumxiLpnnQyYrcuhzAplJf8kmq02uQH2dzWYX74Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"817bce61dc0c31e947b157d978231243bd2498b6a8e041888e798efe8729a029","last_reissued_at":"2026-07-05T09:13:02.588763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:02.588763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe Diffusion-based Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Hao Luan, Harold Soh, Pranav Goyal, Zeyu Feng","submitted_at":"2024-05-07T11:54:22Z","abstract_excerpt":"Operating effectively in complex environments while complying with specified constraints is crucial for the safe and successful deployment of robots that interact with and operate around people. In this work, we focus on generating long-horizon trajectories that adhere to novel static and temporally-extended constraints/instructions at test time. We propose a data-driven diffusion-based framework, LTLDoG, that modifies the inference steps of the reverse process given an instruction specified using finite linear temporal logic ($\\text{LTL}_f$). LTLDoG leverages a satisfaction value function on "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.04235","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/2405.04235/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":"2405.04235","created_at":"2026-07-05T09:13:02.588816+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.04235v2","created_at":"2026-07-05T09:13:02.588816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04235","created_at":"2026-07-05T09:13:02.588816+00:00"},{"alias_kind":"pith_short_12","alias_value":"QF544YO4BQY6","created_at":"2026-07-05T09:13:02.588816+00:00"},{"alias_kind":"pith_short_16","alias_value":"QF544YO4BQY6SR5R","created_at":"2026-07-05T09:13:02.588816+00:00"},{"alias_kind":"pith_short_8","alias_value":"QF544YO4","created_at":"2026-07-05T09:13:02.588816+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.00562","citing_title":"TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO","json":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO.json","graph_json":"https://pith.science/api/pith-number/QF544YO4BQY6SR5RK7MXQIYSIO/graph.json","events_json":"https://pith.science/api/pith-number/QF544YO4BQY6SR5RK7MXQIYSIO/events.json","paper":"https://pith.science/paper/QF544YO4"},"agent_actions":{"view_html":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO","download_json":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO.json","view_paper":"https://pith.science/paper/QF544YO4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.04235&json=true","fetch_graph":"https://pith.science/api/pith-number/QF544YO4BQY6SR5RK7MXQIYSIO/graph.json","fetch_events":"https://pith.science/api/pith-number/QF544YO4BQY6SR5RK7MXQIYSIO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO/action/storage_attestation","attest_author":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO/action/author_attestation","sign_citation":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO/action/citation_signature","submit_replication":"https://pith.science/pith/QF544YO4BQY6SR5RK7MXQIYSIO/action/replication_record"}},"created_at":"2026-07-05T09:13:02.588816+00:00","updated_at":"2026-07-05T09:13:02.588816+00:00"}