{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:UB2Y5OL3ENRWYSKIJFAOO7DSQC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"87dbc93e30dbef1b78a49e3035a4825cb3edd6fceeb58804cfa0b3aa8f6dc0ed","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-03-12T17:56:29Z","title_canon_sha256":"d43d5cf8b40f1cb80969e69ffacd50a77b984f229d5a5e9f012443e282536e28"},"schema_version":"1.0","source":{"id":"2603.12243","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.12243","created_at":"2026-05-20T00:02:10Z"},{"alias_kind":"arxiv_version","alias_value":"2603.12243v4","created_at":"2026-05-20T00:02:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.12243","created_at":"2026-05-20T00:02:10Z"},{"alias_kind":"pith_short_12","alias_value":"UB2Y5OL3ENRW","created_at":"2026-05-20T00:02:10Z"},{"alias_kind":"pith_short_16","alias_value":"UB2Y5OL3ENRWYSKI","created_at":"2026-05-20T00:02:10Z"},{"alias_kind":"pith_short_8","alias_value":"UB2Y5OL3","created_at":"2026-05-20T00:02:10Z"}],"graph_snapshots":[{"event_id":"sha256:8afe083c6f99b84fab15e0e9c8f37970e34526a93d4c815a03e944800b1dd7ee","target":"graph","created_at":"2026-05-20T00:02:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Through extensive hardware experiments across five recognized songs, we demonstrate that HandelBot can successfully perform precise bimanual piano playing. Our system outperforms direct simulation deployment by a factor of 1.8x and requires only 30 minutes of physical interaction data."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That a simulation-trained policy provides a sufficiently close starting point for the structured refinement stage to correct spatial misalignments to millimeter precision using only limited physical rollouts without introducing new instabilities in bimanual coordination."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"HandelBot achieves precise bimanual piano playing by refining a simulation policy through lateral finger adjustments and residual RL, outperforming direct sim deployment by 1.8x with only 30 minutes of physical data across five songs."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"HandelBot adapts a simulation policy in two stages to let a dexterous robot play piano accurately after 30 minutes of real data."}],"snapshot_sha256":"21fd8c0c1f441cf68984ff2ad4d63af70cf43059fe47a8aee850d2f41c323515"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b4481a22bb3f504b786eae7b262dea5a80245b774a2be7314c6913984bd46814"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2603.12243/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mastering dexterous manipulation with multi-fingered hands has been a grand challenge in robotics for decades. Despite its potential, the difficulty of collecting high-quality data remains a primary bottleneck for high-precision tasks. While reinforcement learning and simulation-to-real-world transfer offer a promising alternative, the transferred policies often fail for tasks demanding millimeter-scale precision, such as bimanual piano playing. In this work, we introduce HandelBot, a framework that combines a simulation policy and rapid adaptation through a two-stage pipeline. Starting from a","authors_text":"Amber Xie, Dorsa Sadigh, Haozhi Qi","cross_cats":[],"headline":"HandelBot adapts a simulation policy in two stages to let a dexterous robot play piano accurately after 30 minutes of real data.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-03-12T17:56:29Z","title":"HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.12243","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-15T11:45:02.148110Z","id":"3c5b6be6-2a2a-4f52-8aa1-a643c321fb02","model_set":{"reader":"grok-4.3"},"one_line_summary":"HandelBot achieves precise bimanual piano playing by refining a simulation policy through lateral finger adjustments and residual RL, outperforming direct sim deployment by 1.8x with only 30 minutes of physical data across five songs.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"HandelBot adapts a simulation policy in two stages to let a dexterous robot play piano accurately after 30 minutes of real data.","strongest_claim":"Through extensive hardware experiments across five recognized songs, we demonstrate that HandelBot can successfully perform precise bimanual piano playing. Our system outperforms direct simulation deployment by a factor of 1.8x and requires only 30 minutes of physical interaction data.","weakest_assumption":"That a simulation-trained policy provides a sufficiently close starting point for the structured refinement stage to correct spatial misalignments to millimeter precision using only limited physical rollouts without introducing new instabilities in bimanual coordination."}},"verdict_id":"3c5b6be6-2a2a-4f52-8aa1-a643c321fb02"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f601203cab96626d23c599cdf110765ead21dc32d85f94a8fe01a77f919774bf","target":"record","created_at":"2026-05-20T00:02:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"87dbc93e30dbef1b78a49e3035a4825cb3edd6fceeb58804cfa0b3aa8f6dc0ed","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-03-12T17:56:29Z","title_canon_sha256":"d43d5cf8b40f1cb80969e69ffacd50a77b984f229d5a5e9f012443e282536e28"},"schema_version":"1.0","source":{"id":"2603.12243","kind":"arxiv","version":4}},"canonical_sha256":"a0758eb97b23636c49484940e77c728096b310df0c17b773aa38cda4122e7f1a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a0758eb97b23636c49484940e77c728096b310df0c17b773aa38cda4122e7f1a","first_computed_at":"2026-05-20T00:02:10.502441Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:10.502441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rAuOauLH8nLf/H6FPiCGES/Z8byiutJWwIHmmzzvbKyy39QAh2E76OsG/OBPoH2EX/PsiXW2H9fFVFSXHYdMAg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:10.503303Z","signed_message":"canonical_sha256_bytes"},"source_id":"2603.12243","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f601203cab96626d23c599cdf110765ead21dc32d85f94a8fe01a77f919774bf","sha256:8afe083c6f99b84fab15e0e9c8f37970e34526a93d4c815a03e944800b1dd7ee"],"state_sha256":"a314b4b7aa85b60f58dcdfb3658cbc54af6ab86103cc582d62321dbe269ac113"}