Pith Number

pith:DJ4WHSNJ

pith:2026:DJ4WHSNJUGOXQLK7ALXHAL3BEY

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Automated Curriculum Design for High-dimensional Human Motor Learning

Ankur Kamboj, Rajiv Ranganathan, Vaibhav Srivastava, Xiaobo Tan

A framework using motor learning models and stochastic nonlinear MPC designs curricula that speed skill acquisition by about 23 percent over random schedules.

arxiv:2605.14367 v1 · 2026-05-14 · eess.SY · cs.HC · cs.SY · math.OC

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\pithnumber{DJ4WHSNJUGOXQLK7ALXHAL3BEY}

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Our proposed approach accelerates skill acquisition by ∼23%, and ∼17% when compared to a random curriculum and a performance heuristics-based curriculum, respectively.

C2weakest 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.

C3one 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.

References

36 extracted · 36 resolved · 1 Pith anchors

[1] R. A. Schmidt, T. D. Lee, C. Winstein, G. Wulf, and H. N. Zelaznik, Motor Control and Learning: A Behavioral Emphasis . Human Kinetics, 2018 2018

[2] Individualized challenge point practice as a method to aid motor sequence learning, 2019

[3] Dynamic diﬀiculty adjustment (DDA) in computer games: A review, 2018

[4] Learning control in robot-assisted rehabilitation of motor skills: A review, 2016

[5] Interactive curriculum learning increases and homogenizes motor smoothness, 2024

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:07.874017Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1a7963c9a9a19d782d5f02ee702f61263a02dd34fa0029cf29690d496f828a59

Aliases

arxiv: 2605.14367 · arxiv_version: 2605.14367v1 · doi: 10.48550/arxiv.2605.14367 · pith_short_12: DJ4WHSNJUGOX · pith_short_16: DJ4WHSNJUGOXQLK7 · pith_short_8: DJ4WHSNJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DJ4WHSNJUGOXQLK7ALXHAL3BEY \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1a7963c9a9a19d782d5f02ee702f61263a02dd34fa0029cf29690d496f828a59

Canonical record JSON

{
  "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
  }
}