{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XLMTWIDUY3ACCPLWYWAKLW4CFB","short_pith_number":"pith:XLMTWIDU","schema_version":"1.0","canonical_sha256":"bad93b2074c6c0213d76c580a5db8228555ae2546c6eabb5ce1c54c7a42d42b6","source":{"kind":"arxiv","id":"2410.14868","version":4},"attestation_state":"computed","paper":{"title":"Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Sung-Wook Lee, Xuhui Kang, Yen-Ling Kuo","submitted_at":"2024-10-18T21:28:50Z","abstract_excerpt":"Recently, diffusion policy has shown impressive results in handling multi-modal tasks in robotic manipulation. However, it has fundamental limitations in out-of-distribution failures that persist due to compounding errors and its limited capability to extrapolate. One way to address these limitations is robot-gated DAgger, an interactive imitation learning with a robot query system to actively seek expert help during policy rollout. While robot-gated DAgger has high potential for learning at scale, existing methods like Ensemble-DAgger struggle with highly expressive policies: They often misin"},"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":"2410.14868","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-10-18T21:28:50Z","cross_cats_sorted":[],"title_canon_sha256":"32a03cbe0107168a7405310f683eb29fd48e76b82e541929413a30c3bb86c1da","abstract_canon_sha256":"f806204d4b226c8442b58cd13ab733a9188ab5810afa993974dfa5c108993a18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:35.368847Z","signature_b64":"F3CFyew4S44soSXltlxx0Zfb5MMPII0dnjfalJv0evIVrLZOIHAH4hq2zADI9oMd1NsspZv4CYwVVJ3ccR/rCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bad93b2074c6c0213d76c580a5db8228555ae2546c6eabb5ce1c54c7a42d42b6","last_reissued_at":"2026-07-05T10:37:35.367526Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:35.367526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Sung-Wook Lee, Xuhui Kang, Yen-Ling Kuo","submitted_at":"2024-10-18T21:28:50Z","abstract_excerpt":"Recently, diffusion policy has shown impressive results in handling multi-modal tasks in robotic manipulation. However, it has fundamental limitations in out-of-distribution failures that persist due to compounding errors and its limited capability to extrapolate. One way to address these limitations is robot-gated DAgger, an interactive imitation learning with a robot query system to actively seek expert help during policy rollout. While robot-gated DAgger has high potential for learning at scale, existing methods like Ensemble-DAgger struggle with highly expressive policies: They often misin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14868","kind":"arxiv","version":4},"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/2410.14868/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":"2410.14868","created_at":"2026-07-05T10:37:35.367607+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.14868v4","created_at":"2026-07-05T10:37:35.367607+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14868","created_at":"2026-07-05T10:37:35.367607+00:00"},{"alias_kind":"pith_short_12","alias_value":"XLMTWIDUY3AC","created_at":"2026-07-05T10:37:35.367607+00:00"},{"alias_kind":"pith_short_16","alias_value":"XLMTWIDUY3ACCPLW","created_at":"2026-07-05T10:37:35.367607+00:00"},{"alias_kind":"pith_short_8","alias_value":"XLMTWIDU","created_at":"2026-07-05T10:37:35.367607+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23617","citing_title":"RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03847","citing_title":"Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2502.07645","citing_title":"From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB","json":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB.json","graph_json":"https://pith.science/api/pith-number/XLMTWIDUY3ACCPLWYWAKLW4CFB/graph.json","events_json":"https://pith.science/api/pith-number/XLMTWIDUY3ACCPLWYWAKLW4CFB/events.json","paper":"https://pith.science/paper/XLMTWIDU"},"agent_actions":{"view_html":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB","download_json":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB.json","view_paper":"https://pith.science/paper/XLMTWIDU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.14868&json=true","fetch_graph":"https://pith.science/api/pith-number/XLMTWIDUY3ACCPLWYWAKLW4CFB/graph.json","fetch_events":"https://pith.science/api/pith-number/XLMTWIDUY3ACCPLWYWAKLW4CFB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB/action/storage_attestation","attest_author":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB/action/author_attestation","sign_citation":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB/action/citation_signature","submit_replication":"https://pith.science/pith/XLMTWIDUY3ACCPLWYWAKLW4CFB/action/replication_record"}},"created_at":"2026-07-05T10:37:35.367607+00:00","updated_at":"2026-07-05T10:37:35.367607+00:00"}