{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6TFFNIFUB5LSRGIVE5STYARII3","short_pith_number":"pith:6TFFNIFU","schema_version":"1.0","canonical_sha256":"f4ca56a0b40f5728991527653c022846c4904c1cd851e2a4ab7b75ad717bcea4","source":{"kind":"arxiv","id":"2410.22689","version":1},"attestation_state":"computed","paper":{"title":"Multi-Task Interactive Robot Fleet Learning with Visual World Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Crystal Ding, Evan Zhang, Huihan Liu, James Liu, Vaarij Betala, Yuke Zhu, Yu Zhang","submitted_at":"2024-10-30T04:49:39Z","abstract_excerpt":"Recent advancements in large-scale multi-task robot learning offer the potential for deploying robot fleets in household and industrial settings, enabling them to perform diverse tasks across various environments. However, AI-enabled robots often face challenges with generalization and robustness when exposed to real-world variability and uncertainty. We introduce Sirius-Fleet, a multi-task interactive robot fleet learning framework to address these challenges. Sirius-Fleet monitors robot performance during deployment and involves humans to correct the robot's actions when necessary. We employ"},"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":"2410.22689","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-30T04:49:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2246b84836d5571aa1065e3aa19c71af31ada3a571cad3be8b7cfc5750b08b06","abstract_canon_sha256":"12de8a09bd88cd644a3f1971cdeb5a57a5cb08ea5a8045407ade2cbc4aa44228"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:20.247626Z","signature_b64":"DXylCWsIApzphKoL6tLzjp5OYfr9QZ2ZncUbUR3QC0ogEyYskvKzqGJL0N7OGDFk2DhK8G229ECaWB0sQiH8Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4ca56a0b40f5728991527653c022846c4904c1cd851e2a4ab7b75ad717bcea4","last_reissued_at":"2026-07-05T09:28:20.247204Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:20.247204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Task Interactive Robot Fleet Learning with Visual World Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Crystal Ding, Evan Zhang, Huihan Liu, James Liu, Vaarij Betala, Yuke Zhu, Yu Zhang","submitted_at":"2024-10-30T04:49:39Z","abstract_excerpt":"Recent advancements in large-scale multi-task robot learning offer the potential for deploying robot fleets in household and industrial settings, enabling them to perform diverse tasks across various environments. However, AI-enabled robots often face challenges with generalization and robustness when exposed to real-world variability and uncertainty. We introduce Sirius-Fleet, a multi-task interactive robot fleet learning framework to address these challenges. Sirius-Fleet monitors robot performance during deployment and involves humans to correct the robot's actions when necessary. We employ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.22689","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/2410.22689/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":"2410.22689","created_at":"2026-07-05T09:28:20.247263+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.22689v1","created_at":"2026-07-05T09:28:20.247263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.22689","created_at":"2026-07-05T09:28:20.247263+00:00"},{"alias_kind":"pith_short_12","alias_value":"6TFFNIFUB5LS","created_at":"2026-07-05T09:28:20.247263+00:00"},{"alias_kind":"pith_short_16","alias_value":"6TFFNIFUB5LSRGIV","created_at":"2026-07-05T09:28:20.247263+00:00"},{"alias_kind":"pith_short_8","alias_value":"6TFFNIFU","created_at":"2026-07-05T09:28:20.247263+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23085","citing_title":"Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08508","citing_title":"ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3","json":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3.json","graph_json":"https://pith.science/api/pith-number/6TFFNIFUB5LSRGIVE5STYARII3/graph.json","events_json":"https://pith.science/api/pith-number/6TFFNIFUB5LSRGIVE5STYARII3/events.json","paper":"https://pith.science/paper/6TFFNIFU"},"agent_actions":{"view_html":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3","download_json":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3.json","view_paper":"https://pith.science/paper/6TFFNIFU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.22689&json=true","fetch_graph":"https://pith.science/api/pith-number/6TFFNIFUB5LSRGIVE5STYARII3/graph.json","fetch_events":"https://pith.science/api/pith-number/6TFFNIFUB5LSRGIVE5STYARII3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3/action/storage_attestation","attest_author":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3/action/author_attestation","sign_citation":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3/action/citation_signature","submit_replication":"https://pith.science/pith/6TFFNIFUB5LSRGIVE5STYARII3/action/replication_record"}},"created_at":"2026-07-05T09:28:20.247263+00:00","updated_at":"2026-07-05T09:28:20.247263+00:00"}