Does the 2D hanger result survive in 3D, with front and back panels and a hanger arm of finite depth?
research/hanger-3d
Does the 2D hanger result survive in 3D? With an arm as deep as the garment a 3D cloth sheet matches the 2D drape (within 0.05 cm for round-ended hangers) and the best width is again 1 cm over the half shoulder; a shallow arm lets the front and back panels fall 5–8 cm and raises a wooden hanger's error from 0.4 to 2.0 cm. Stylised.
What it found
A 3D cloth sheet (stretch, shear and bending in both directions, penalty contact with a swept arm) reproduces the 2D hanger-fit drape when the arm is as deep as the garment: shape error within 0.05 cm for the round-ended and wooden hangers and within 0.6 cm for thin wire (which is resolution-sensitive). The width finding holds: the best arm is 1 cm wider than the half shoulder for child, M and XXL tees, in 2D and 3D. What 2D cannot show is depth: on a 3 cm half-depth arm the wooden hanger's whole-sheet error rises from 0.4 to 2.0 cm, and on a rod to 3.8 cm, as the front and back panels fall 5–8 cm below the arm. A CMA-ES design loop rerun in 3D (M tee, 64 drapes, two seeds) shows arm depth sets the whole-sheet error floor (about 2.1, 1.5 and 0.7 cm at 1.5, 3 and 6 cm half-depth) while shape matters less: hangers designed on the 2D centre line are within seed noise of 3D-designed ones. Stylised: assumed fabric constants, one 20 cm strip, coarse grid (0.5 cm by 2.2 cm), no collar, sleeve seam or friction.
A figure from its own output
Paper, code and results
More in Laundry-hanging robots
- Does a 3D-designed hanger keep improving when the end-radius cap is lifted past 6 cm?No: with caps of 10 and 15 cm the whole-sheet error is 1.08 and 1.21 cm against 1.12 cm at 6 cm (8 seeds each, p = 0.77 and 0.57), so the optimum sat on a flat ridge. Stylised.
- Is a hanger designed in 3D really better than one designed on the 2D centre line, once seed noise is counted?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.
- Does a second laundry-hanging arm lose less if it picks a free garment instead of waiting?Does a second laundry-hanging arm lose less if it picks a free garment instead of waiting? At 0.30 m clearance blind pinching recovers 9.5 of its 28 lost garments an hour (151 to 161), aiming about 2 of 10; at 0.45 m nothing. Stylised: clearance and spacing assumed.
- Is a laundry-hanging arm's gain from picking a free garment real? A paired test on 2000 loadsIs a laundry-hanging arm's gain from picking a free garment real? Paired on 2000 loads at 0.30 m: aiming +1.25 an hour (0.60 to 1.92), sweeping +4.4, pinching +10.0; at 0.45 m aiming gains nothing. Stylised: inputs are placeholders.
- How many dryers can one laundry-hanging arm serve once the pick is simulated, not assumed?How many dryers can one laundry-hanging arm serve once the pick is simulated, not assumed? A one-arm cell drops from 169 to 147 garments an hour (5.1 to 4.4 dryers) with look-then-aim picking and to 104 with blind pinching; with two arms the fold table is the limit. Stylised.
- How many dryers can a laundry-hanging arm serve when the fold takes as long as published robots take?How many dryers can a laundry-hanging arm serve when folding takes as long as published robots take? At SpeedFolding's 90–120 s per fold the learned-rule cell falls from 169 to 58–44 garments an hour; always hanging holds 146. Only the fold time is sourced.
All 25 Laundry-hanging robots projects →
Other research topics
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.