Pith Number

pith:XTYUDGOZ

pith:2026:XTYUDGOZ4Y3CACLKJREWES4ZI4

not attested not anchored not stored refs resolved

Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control

Chris Atkeson, Krishna Suresh

Task-level iterative learning control lets robots master dynamic flying knots from one human demonstration and a simplified rope model.

arxiv:2602.21302 v2 · 2026-02-24 · cs.RO

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{XTYUDGOZ4Y3CACLKJREWES4ZI4}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

Using a single human demonstration and a simplified rope model, the method learns directly on hardware without reliance on large amounts of demonstration data or massive amounts of simulation. Learning achieves a 100% success rate within 10 trials on all ropes. Furthermore, the method can successfully transfer between most rope types in 2--5 trials.

C2weakest assumption

The simplified rope model is sufficient to allow quadratic program inversion to accurately propagate task-space errors into effective action updates for the dynamic flying knot task.

C3one line summary

Task-level ILC learns flying knot rope manipulation from one demo, achieving 100% success within 10 trials on 7 rope types with 2-5 trial transfers.

References

51 extracted · 51 resolved · 0 Pith anchors

[1] Using inaccurate models in reinforcement learning 2006

[2] The enhanced compact qp method for redundant manipulators using practical inequality constraints 1988 · doi:10.1109/robot

[3] A regret minimization approach to iterative learning control

[4] URL https://proceedings.mlr.press/v139/ agarwal21b.html

[5] Chae H. An, Christopher G. Atkeson, and John Holler- bach.Model-Based Control of a Robot Manipulator. Artificial Intelligence Series. MIT Press. ISBN 978-0- 262-51157-5

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:15.978499Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

bcf14199d9e63620096a4c49624b99473bb6173776c375f48b1faede1a3d7ec6

Aliases

arxiv: 2602.21302 · arxiv_version: 2602.21302v2 · doi: 10.48550/arxiv.2602.21302 · pith_short_12: XTYUDGOZ4Y3C · pith_short_16: XTYUDGOZ4Y3CACLK · pith_short_8: XTYUDGOZ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/XTYUDGOZ4Y3CACLKJREWES4ZI4 \
  | 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: bcf14199d9e63620096a4c49624b99473bb6173776c375f48b1faede1a3d7ec6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "59ac72354d0f28df366edfe84cf6a6214012436fce33b56d9d2a7820cf005497",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-02-24T19:13:25Z",
    "title_canon_sha256": "ab111a331b479784dd3af2f490c6a4753edb0978f18a744b9a85e783a2cb8663"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.21302",
    "kind": "arxiv",
    "version": 2
  }
}