{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DJ4WHSNJUGOXQLK7ALXHAL3BEY","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":"2b881c58aaf6cc1171e35465940d0da0da669c3bf9ce5622933937189bec5d9c","cross_cats_sorted":["cs.HC","cs.SY","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-05-14T04:47:24Z","title_canon_sha256":"f420e43c69bbfab996e426bbcce5a3f56d77d10f14be8ea8e7fa1089faa18de0"},"schema_version":"1.0","source":{"id":"2605.14367","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14367","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14367v1","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14367","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"pith_short_12","alias_value":"DJ4WHSNJUGOX","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"DJ4WHSNJUGOXQLK7","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"DJ4WHSNJ","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:5cf0844eecf88abfb62f8c18c522287736882c1298223dedb308ba98a0934d49","target":"graph","created_at":"2026-05-17T23:39:07Z","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":"Our proposed approach accelerates skill acquisition by ∼23%, and ∼17% when compared to a random curriculum and a performance heuristics-based curriculum, respectively."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The human motor learning model combined with real-time skill estimation accurately captures unobservable skill states in de-novo tasks, allowing the stochastic nonlinear MPC to select effective curricula."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A model-based curriculum using stochastic nonlinear MPC and real-time skill estimation accelerates high-dimensional motor learning by ~23% versus random schedules and ~17% versus performance-based heuristics in simulations and N=36 human experiments with a hand exoskeleton."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A framework using motor learning models and stochastic nonlinear MPC designs curricula that speed skill acquisition by about 23 percent over random schedules."}],"snapshot_sha256":"85c6219b315045cef1681a55cdb8652ca4b411783c91e6b3b8d25b492ee4caab"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c48d92d99eb58ab8536e153efffbb55879e66513f30398e85cf6d70da74057d8"},"paper":{"abstract_excerpt":"Designing effective practice schedules for high-dimensional motor learning tasks remains a challenge, especially when skill states are unobservable and task performance may not reflect the true learning. We propose an automated curriculum design framework that combines a human motor learning model and personalized real-time skill estimation with Stochastic Nonlinear Model Predictive Control in \\emph{de-novo} (novel) motor learning paradigms. We validated our framework both through simulations and human-subject studies (N = 36) using a hand exoskeleton. Our proposed approach accelerates skill a","authors_text":"Ankur Kamboj, Rajiv Ranganathan, Vaibhav Srivastava, Xiaobo Tan","cross_cats":["cs.HC","cs.SY","math.OC"],"headline":"A framework using motor learning models and stochastic nonlinear MPC designs curricula that speed skill acquisition by about 23 percent over random schedules.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-05-14T04:47:24Z","title":"Automated Curriculum Design for High-dimensional Human Motor Learning"},"references":{"count":36,"internal_anchors":1,"resolved_work":36,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"R. A. Schmidt, T. D. Lee, C. Winstein, G. Wulf, and H. N. Zelaznik, Motor Control and Learning: A Behavioral Emphasis . Human Kinetics, 2018","work_id":"eb863647-c8ee-411e-a1e2-20ab6b500bb7","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Individualized challenge point practice as a method to aid motor sequence learning,","work_id":"84894eeb-c1e7-4b91-a7c1-ed0c8827afff","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Dynamic diﬀiculty adjustment (DDA) in computer games: A review,","work_id":"f312cde9-6d25-46a6-98e4-939b1f8b1af8","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Learning control in robot-assisted rehabilitation of motor skills: A review,","work_id":"04d8bb19-d5b1-4dbb-9cda-df770c6eef7f","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Interactive curriculum learning increases and homogenizes motor smoothness,","work_id":"415e2b6f-edff-455b-9c87-8b223cfca615","year":2024}],"snapshot_sha256":"e5508f6f964b8dfe75d77350969d685ceb50cb35ec470f9020f7c695fa39e45d"},"source":{"id":"2605.14367","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T02:31:58.003011Z","id":"fa714de5-4581-49d1-8eca-ef12610944c4","model_set":{"reader":"grok-4.3"},"one_line_summary":"A model-based curriculum using stochastic nonlinear MPC and real-time skill estimation accelerates high-dimensional motor learning by ~23% versus random schedules and ~17% versus performance-based heuristics in simulations and N=36 human experiments with a hand exoskeleton.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A framework using motor learning models and stochastic nonlinear MPC designs curricula that speed skill acquisition by about 23 percent over random schedules.","strongest_claim":"Our proposed approach accelerates skill acquisition by ∼23%, and ∼17% when compared to a random curriculum and a performance heuristics-based curriculum, respectively.","weakest_assumption":"The human motor learning model combined with real-time skill estimation accurately captures unobservable skill states in de-novo tasks, allowing the stochastic nonlinear MPC to select effective curricula."}},"verdict_id":"fa714de5-4581-49d1-8eca-ef12610944c4"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0c460d8829840e22d5c01818aef7403bca7d6010053a88c10fcf085d9f6512d3","target":"record","created_at":"2026-05-17T23:39:07Z","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":"2b881c58aaf6cc1171e35465940d0da0da669c3bf9ce5622933937189bec5d9c","cross_cats_sorted":["cs.HC","cs.SY","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-05-14T04:47:24Z","title_canon_sha256":"f420e43c69bbfab996e426bbcce5a3f56d77d10f14be8ea8e7fa1089faa18de0"},"schema_version":"1.0","source":{"id":"2605.14367","kind":"arxiv","version":1}},"canonical_sha256":"1a7963c9a9a19d782d5f02ee702f61263a02dd34fa0029cf29690d496f828a59","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a7963c9a9a19d782d5f02ee702f61263a02dd34fa0029cf29690d496f828a59","first_computed_at":"2026-05-17T23:39:07.874017Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:07.874017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QcQ7recmknItVA2JVElPG4vHx60f8ieV4QJMga2Z0vV3RZptZi+JDnZuig2JHqaBMwwjVCL28IY7CXVgtH8OAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:07.874807Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14367","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c460d8829840e22d5c01818aef7403bca7d6010053a88c10fcf085d9f6512d3","sha256:5cf0844eecf88abfb62f8c18c522287736882c1298223dedb308ba98a0934d49"],"state_sha256":"2970af5e1682754426eb39e519722d3511415db2cacce0e827da2b10a56294f4"}