{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ITHS23WFOLYCQF523XLCA67PKR","short_pith_number":"pith:ITHS23WF","schema_version":"1.0","canonical_sha256":"44cf2d6ec572f02817baddd6207bef54695e3d9293a93ef4f85cd9e3969337d1","source":{"kind":"arxiv","id":"2207.01840","version":1},"attestation_state":"computed","paper":{"title":"Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Eiji Uchibe, Jun Morimoto, Takamitsu Matsubara, Tomoya Yamanokuchi, Yoshihisa Tsurumine, Yuhwan Kwon","submitted_at":"2022-07-05T07:05:46Z","abstract_excerpt":"Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the models learned in simulations to new domains in the real world. To alleviate this problem, we first propose a novel model-learning framework called Kalman Randomized-to-Canonical Model (KRC-model). This framework is capable of extracting task-relevant intrinsic features and their dynamics from randomized"},"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":"2207.01840","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-07-05T07:05:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3f522517185755bd1a5ea5987a13a8e54e4c27e152cace153c4a973331a79e09","abstract_canon_sha256":"c92c62a432fe609aedf71ec25a93f7a963b91be1856db0aa1ed4d6d95119e7e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:37:44.970436Z","signature_b64":"gB3NUL+9yfb9bjs3PxQkpLjaZ1ZRoND9WEepA2C1bzsZkP3zwIq4gVyLqh6nhS/r1bwqPmAQs1DArZz2IUVqDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44cf2d6ec572f02817baddd6207bef54695e3d9293a93ef4f85cd9e3969337d1","last_reissued_at":"2026-07-05T04:37:44.970080Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:37:44.970080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Eiji Uchibe, Jun Morimoto, Takamitsu Matsubara, Tomoya Yamanokuchi, Yoshihisa Tsurumine, Yuhwan Kwon","submitted_at":"2022-07-05T07:05:46Z","abstract_excerpt":"Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the models learned in simulations to new domains in the real world. To alleviate this problem, we first propose a novel model-learning framework called Kalman Randomized-to-Canonical Model (KRC-model). This framework is capable of extracting task-relevant intrinsic features and their dynamics from randomized"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.01840","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/2207.01840/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":"2207.01840","created_at":"2026-07-05T04:37:44.970129+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.01840v1","created_at":"2026-07-05T04:37:44.970129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.01840","created_at":"2026-07-05T04:37:44.970129+00:00"},{"alias_kind":"pith_short_12","alias_value":"ITHS23WFOLYC","created_at":"2026-07-05T04:37:44.970129+00:00"},{"alias_kind":"pith_short_16","alias_value":"ITHS23WFOLYCQF52","created_at":"2026-07-05T04:37:44.970129+00:00"},{"alias_kind":"pith_short_8","alias_value":"ITHS23WF","created_at":"2026-07-05T04:37:44.970129+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/ITHS23WFOLYCQF523XLCA67PKR","json":"https://pith.science/pith/ITHS23WFOLYCQF523XLCA67PKR.json","graph_json":"https://pith.science/api/pith-number/ITHS23WFOLYCQF523XLCA67PKR/graph.json","events_json":"https://pith.science/api/pith-number/ITHS23WFOLYCQF523XLCA67PKR/events.json","paper":"https://pith.science/paper/ITHS23WF"},"agent_actions":{"view_html":"https://pith.science/pith/ITHS23WFOLYCQF523XLCA67PKR","download_json":"https://pith.science/pith/ITHS23WFOLYCQF523XLCA67PKR.json","view_paper":"https://pith.science/paper/ITHS23WF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.01840&json=true","fetch_graph":"https://pith.science/api/pith-number/ITHS23WFOLYCQF523XLCA67PKR/graph.json","fetch_events":"https://pith.science/api/pith-number/ITHS23WFOLYCQF523XLCA67PKR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ITHS23WFOLYCQF523XLCA67PKR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ITHS23WFOLYCQF523XLCA67PKR/action/storage_attestation","attest_author":"https://pith.science/pith/ITHS23WFOLYCQF523XLCA67PKR/action/author_attestation","sign_citation":"https://pith.science/pith/ITHS23WFOLYCQF523XLCA67PKR/action/citation_signature","submit_replication":"https://pith.science/pith/ITHS23WFOLYCQF523XLCA67PKR/action/replication_record"}},"created_at":"2026-07-05T04:37:44.970129+00:00","updated_at":"2026-07-05T04:37:44.970129+00:00"}