{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:47KGLHZNRE2MUREBLQCOTKC6MG","short_pith_number":"pith:47KGLHZN","schema_version":"1.0","canonical_sha256":"e7d4659f2d8934ca44815c04e9a85e619e67dc925c8677e3ead71c959da72bda","source":{"kind":"arxiv","id":"2502.01167","version":1},"attestation_state":"computed","paper":{"title":"ConditionNET: Learning Preconditions and Effects for Execution Monitoring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Daniel Sliwowski, Dongheui Lee","submitted_at":"2025-02-03T09:00:45Z","abstract_excerpt":"The introduction of robots into everyday scenarios necessitates algorithms capable of monitoring the execution of tasks. In this paper, we propose ConditionNET, an approach for learning the preconditions and effects of actions in a fully data-driven manner. We develop an efficient vision-language model and introduce additional optimization objectives during training to optimize for consistent feature representations. ConditionNET explicitly models the dependencies between actions, preconditions, and effects, leading to improved performance. We evaluate our model on two robotic datasets, one of"},"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":"2502.01167","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-03T09:00:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a6f18e4fbd129d76c78e708e187144e2a617bb306b108c045199fd9057ee5bb5","abstract_canon_sha256":"0a825f760a3714a7d00e6af77dadb3a415ecfb66b008e200be95cbf9ae590281"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:43.586108Z","signature_b64":"wIRMPQK1+7p469AI9n/DgLSIQbc0y0ZX7HWHeSCnvVLDRC+pANN6WncMiSi4AOWPNyPFO9E1QCBX5VJwjKNkCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7d4659f2d8934ca44815c04e9a85e619e67dc925c8677e3ead71c959da72bda","last_reissued_at":"2026-07-05T10:08:43.585697Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:43.585697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ConditionNET: Learning Preconditions and Effects for Execution Monitoring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Daniel Sliwowski, Dongheui Lee","submitted_at":"2025-02-03T09:00:45Z","abstract_excerpt":"The introduction of robots into everyday scenarios necessitates algorithms capable of monitoring the execution of tasks. In this paper, we propose ConditionNET, an approach for learning the preconditions and effects of actions in a fully data-driven manner. We develop an efficient vision-language model and introduce additional optimization objectives during training to optimize for consistent feature representations. ConditionNET explicitly models the dependencies between actions, preconditions, and effects, leading to improved performance. We evaluate our model on two robotic datasets, one of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01167","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/2502.01167/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":"2502.01167","created_at":"2026-07-05T10:08:43.585755+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01167v1","created_at":"2026-07-05T10:08:43.585755+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01167","created_at":"2026-07-05T10:08:43.585755+00:00"},{"alias_kind":"pith_short_12","alias_value":"47KGLHZNRE2M","created_at":"2026-07-05T10:08:43.585755+00:00"},{"alias_kind":"pith_short_16","alias_value":"47KGLHZNRE2MUREB","created_at":"2026-07-05T10:08:43.585755+00:00"},{"alias_kind":"pith_short_8","alias_value":"47KGLHZN","created_at":"2026-07-05T10:08:43.585755+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG","json":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG.json","graph_json":"https://pith.science/api/pith-number/47KGLHZNRE2MUREBLQCOTKC6MG/graph.json","events_json":"https://pith.science/api/pith-number/47KGLHZNRE2MUREBLQCOTKC6MG/events.json","paper":"https://pith.science/paper/47KGLHZN"},"agent_actions":{"view_html":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG","download_json":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG.json","view_paper":"https://pith.science/paper/47KGLHZN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01167&json=true","fetch_graph":"https://pith.science/api/pith-number/47KGLHZNRE2MUREBLQCOTKC6MG/graph.json","fetch_events":"https://pith.science/api/pith-number/47KGLHZNRE2MUREBLQCOTKC6MG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG/action/storage_attestation","attest_author":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG/action/author_attestation","sign_citation":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG/action/citation_signature","submit_replication":"https://pith.science/pith/47KGLHZNRE2MUREBLQCOTKC6MG/action/replication_record"}},"created_at":"2026-07-05T10:08:43.585755+00:00","updated_at":"2026-07-05T10:08:43.585755+00:00"}