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Laundry-hanging robots

Is a hanger designed in 3D really better than one designed on the 2D centre line, once seed noise is counted?

research/hanger-3dseeds

Yes, by 0.26 cm on the whole sheet with hanger-fit's bounds (8 seeds each, p = 0.03) and by 0.68 cm once the end-radius and bow limits are widened (p < 0.001), but the 3D-designed hangers have a worse centre line. Stylised.

4 tests

What it found

hanger-3d found that a 3D design loop beat hangers designed on the 2D centre line by 0.2–0.4 cm at 3 cm half-depth on two seeds, within seed noise. Here are eight seeds per route (M tee, 3 cm half-depth arm, 64 drapes per 3D loop, 150 per 2D loop). Whole-sheet error: 2D-designed 1.80 ± 0.13 cm (mean ± sd), 3D-designed with hanger-fit's bounds 1.54 ± 0.28 (difference −0.26, bootstrap 95% CI −0.44 to −0.03, exact permutation p = 0.033), 3D-designed with bow up to 5 cm and end radius up to 6 cm 1.12 ± 0.25 (−0.68 against 2D, CI −0.85 to −0.49, p < 0.001; −0.42 against the original bounds, p = 0.006). The 3D optimum sits on the radius limit in three of eight wide-bound runs, so the bound still binds. The wide-bound designs also drop the crease from about 25 to 11 deg/cm and the edge sag from 4.6 to 2.9 cm but have a worse centre-line error (0.77 against 0.40 cm), so the 2D score and the whole-sheet score disagree. Raising the radius limit to 9 and 12 cm (8 seeds each) leaves no seed on the limit at 12 cm and changes the error by only −0.10 to −0.11 cm (p > 0.4); the optimum radius ranges 4.8–10.4 cm, so the error is flat in it. Stylised: assumed fabric constants, one 20 cm strip, coarse grid, no friction; one garment and one arm depth; the budget (64 drapes) is small, so a longer search may change the gap (untested).

A figure from its own output

Figure produced by research/hanger-3dseeds: err_by_route
hanger-3dseeds/experiments/err_by_route.svg, written by the project's own experiment code.

Paper, code and results

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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.