{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ETISYNXCW65ZDSVQJNNG6C6NYA","short_pith_number":"pith:ETISYNXC","schema_version":"1.0","canonical_sha256":"24d12c36e2b7bb91cab04b5a6f0bcdc0006844d19bcb04c74760d9635937e0e2","source":{"kind":"arxiv","id":"2312.01097","version":1},"attestation_state":"computed","paper":{"title":"Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Cheng-Fu Yang, Feng Gao, Haoyang Xu, Kai-Wei Chang, Te-Lin Wu, Xiaofeng Gao","submitted_at":"2023-12-02T10:07:17Z","abstract_excerpt":"Task planning for embodied AI has been one of the most challenging problems where the community does not meet a consensus in terms of formulation. In this paper, we aim to tackle this problem with a unified framework consisting of an end-to-end trainable method and a planning algorithm. Particularly, we propose a task-agnostic method named 'planning as in-painting'. In this method, we use a Denoising Diffusion Model (DDM) for plan generation, conditioned on both language instructions and perceptual inputs under partially observable environments. Partial observation often leads to the model hal"},"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":"2312.01097","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-02T10:07:17Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"018e07f0f5977ea7b3f663519e094e68985e7ce55c136b2a251fc51901547b33","abstract_canon_sha256":"beaf57217543dfdeb08cad5e2522c46152ce8c746d846cc3d7895a4a529ee634"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:29.339980Z","signature_b64":"eXeTc8I1oJBxGR71CEJ778QUWVOvq5yy4WAwnJqLHPEEoh6gf81p4h6mKDzAB6oO3QkhUBOef5IAXqZvPJDWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24d12c36e2b7bb91cab04b5a6f0bcdc0006844d19bcb04c74760d9635937e0e2","last_reissued_at":"2026-07-05T07:19:29.339461Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:29.339461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Cheng-Fu Yang, Feng Gao, Haoyang Xu, Kai-Wei Chang, Te-Lin Wu, Xiaofeng Gao","submitted_at":"2023-12-02T10:07:17Z","abstract_excerpt":"Task planning for embodied AI has been one of the most challenging problems where the community does not meet a consensus in terms of formulation. In this paper, we aim to tackle this problem with a unified framework consisting of an end-to-end trainable method and a planning algorithm. Particularly, we propose a task-agnostic method named 'planning as in-painting'. In this method, we use a Denoising Diffusion Model (DDM) for plan generation, conditioned on both language instructions and perceptual inputs under partially observable environments. Partial observation often leads to the model hal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01097","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/2312.01097/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":"2312.01097","created_at":"2026-07-05T07:19:29.339519+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.01097v1","created_at":"2026-07-05T07:19:29.339519+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01097","created_at":"2026-07-05T07:19:29.339519+00:00"},{"alias_kind":"pith_short_12","alias_value":"ETISYNXCW65Z","created_at":"2026-07-05T07:19:29.339519+00:00"},{"alias_kind":"pith_short_16","alias_value":"ETISYNXCW65ZDSVQ","created_at":"2026-07-05T07:19:29.339519+00:00"},{"alias_kind":"pith_short_8","alias_value":"ETISYNXC","created_at":"2026-07-05T07:19:29.339519+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21646","citing_title":"Energy-based Compositional Diffusion Planning","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA","json":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA.json","graph_json":"https://pith.science/api/pith-number/ETISYNXCW65ZDSVQJNNG6C6NYA/graph.json","events_json":"https://pith.science/api/pith-number/ETISYNXCW65ZDSVQJNNG6C6NYA/events.json","paper":"https://pith.science/paper/ETISYNXC"},"agent_actions":{"view_html":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA","download_json":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA.json","view_paper":"https://pith.science/paper/ETISYNXC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.01097&json=true","fetch_graph":"https://pith.science/api/pith-number/ETISYNXCW65ZDSVQJNNG6C6NYA/graph.json","fetch_events":"https://pith.science/api/pith-number/ETISYNXCW65ZDSVQJNNG6C6NYA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA/action/storage_attestation","attest_author":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA/action/author_attestation","sign_citation":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA/action/citation_signature","submit_replication":"https://pith.science/pith/ETISYNXCW65ZDSVQJNNG6C6NYA/action/replication_record"}},"created_at":"2026-07-05T07:19:29.339519+00:00","updated_at":"2026-07-05T07:19:29.339519+00:00"}