{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:N5MSUGAZNHAUJ6DMZ6JY642MR5","short_pith_number":"pith:N5MSUGAZ","schema_version":"1.0","canonical_sha256":"6f592a181969c144f86ccf938f734c8f6814b181e8286b42fb047f05798b2b8b","source":{"kind":"arxiv","id":"2109.06737","version":1},"attestation_state":"computed","paper":{"title":"Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Anastasia Varava, Constantinos Chamzas, Danica Kragic, Lydia E. Kavraki, Martina Lippi, Michael C. Welle","submitted_at":"2021-09-14T14:52:49Z","abstract_excerpt":"Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space. The similarity between observations in the space of images is often assumed and used as a proxy for estimating similarity between the underlying states of the system. However, observations commonly contain task-irrelevant factors of variation which are nonetheless important for reconstruction, such as varying lighting and different camera view"},"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":"2109.06737","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-09-14T14:52:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7c60bb435add957a7eb5ef07ea0a50cf4e795cbcd3c4b81560c6e29faf21c88e","abstract_canon_sha256":"dfd52a5abcc2236226b96684cf569198fbf7107fa4e18fee443d3b6d688248e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:14:29.665235Z","signature_b64":"VRNcjGkQ10rav6c3/vGOzWL01QrlVMM5hLCsUVXAEkDYX8TzdZVP2IGe23f9dxcPqNMVCToQ1Gyl2w4Bf0hABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f592a181969c144f86ccf938f734c8f6814b181e8286b42fb047f05798b2b8b","last_reissued_at":"2026-07-05T03:14:29.664748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:14:29.664748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Anastasia Varava, Constantinos Chamzas, Danica Kragic, Lydia E. Kavraki, Martina Lippi, Michael C. Welle","submitted_at":"2021-09-14T14:52:49Z","abstract_excerpt":"Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space. The similarity between observations in the space of images is often assumed and used as a proxy for estimating similarity between the underlying states of the system. However, observations commonly contain task-irrelevant factors of variation which are nonetheless important for reconstruction, such as varying lighting and different camera view"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.06737","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/2109.06737/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":"2109.06737","created_at":"2026-07-05T03:14:29.664805+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.06737v1","created_at":"2026-07-05T03:14:29.664805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.06737","created_at":"2026-07-05T03:14:29.664805+00:00"},{"alias_kind":"pith_short_12","alias_value":"N5MSUGAZNHAU","created_at":"2026-07-05T03:14:29.664805+00:00"},{"alias_kind":"pith_short_16","alias_value":"N5MSUGAZNHAUJ6DM","created_at":"2026-07-05T03:14:29.664805+00:00"},{"alias_kind":"pith_short_8","alias_value":"N5MSUGAZ","created_at":"2026-07-05T03:14:29.664805+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/N5MSUGAZNHAUJ6DMZ6JY642MR5","json":"https://pith.science/pith/N5MSUGAZNHAUJ6DMZ6JY642MR5.json","graph_json":"https://pith.science/api/pith-number/N5MSUGAZNHAUJ6DMZ6JY642MR5/graph.json","events_json":"https://pith.science/api/pith-number/N5MSUGAZNHAUJ6DMZ6JY642MR5/events.json","paper":"https://pith.science/paper/N5MSUGAZ"},"agent_actions":{"view_html":"https://pith.science/pith/N5MSUGAZNHAUJ6DMZ6JY642MR5","download_json":"https://pith.science/pith/N5MSUGAZNHAUJ6DMZ6JY642MR5.json","view_paper":"https://pith.science/paper/N5MSUGAZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.06737&json=true","fetch_graph":"https://pith.science/api/pith-number/N5MSUGAZNHAUJ6DMZ6JY642MR5/graph.json","fetch_events":"https://pith.science/api/pith-number/N5MSUGAZNHAUJ6DMZ6JY642MR5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N5MSUGAZNHAUJ6DMZ6JY642MR5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N5MSUGAZNHAUJ6DMZ6JY642MR5/action/storage_attestation","attest_author":"https://pith.science/pith/N5MSUGAZNHAUJ6DMZ6JY642MR5/action/author_attestation","sign_citation":"https://pith.science/pith/N5MSUGAZNHAUJ6DMZ6JY642MR5/action/citation_signature","submit_replication":"https://pith.science/pith/N5MSUGAZNHAUJ6DMZ6JY642MR5/action/replication_record"}},"created_at":"2026-07-05T03:14:29.664805+00:00","updated_at":"2026-07-05T03:14:29.664805+00:00"}