How many dryers can one laundry-hanging robot serve, and what limits it?

research/hanger-cell

How many dryers can one laundry-hanging robot serve, and what limits it? A discrete-event model of the whole cell (pick, sense, feeder, insertion, rail, fold table) fed by hanger-insert and hanger-fold: with assumed times one arm clears a 25-item load in 8.9 min (about 5 dryers), a second arm adds 22% before the fold table binds, and the five hanger designs differ by at most 3%; pick and fold times dominate. Stylised, assumed station times.

12 tests

What it found

A discrete-event model of the whole cell (pick, sense, hanger feeder, insertion, rail and fold table) fed with hanger-insert’s insertion times and hanger-fold’s hang-or-fold shares. With assumed pick and fold times, one arm clears a 25-item dryer load in 8.9 min (169 items/h, about 5 dryers on a 45 min cycle); a second arm adds 22% and then the fold table is the bottleneck. The five hanger designs differ by at most 3% in time per load. Doubling the fold time adds 5.9 min and doubling the pick time 3.4, against under 1 min for insertion, though a threefold slower insertion costs a one-arm cell 22%. A poor picker (0.60 success) costs 13% throughput but leaves 7 of every 100 items for a person. Stylised: every station time except insertion is an assumed placeholder, no layout or collisions.

A figure from its own output

Figure produced by research/hanger-cell: pick_vs_throughput — hanger-cell/experiments/pick_vs_throughput.svg, written by the project's own experiment code.

Paper, code and results

More in Laundry-hanging robots

Can the hanger a cloth simulation designed be 3D-printed, and does it hold a garment?Can the hanger a cloth simulation designed be 3D-printed, and does it hold a garment? hanger-fit's adjustable shape and four-width rack as parametric STL and OpenSCAD with a beam check: the round-ended arm has a safety factor of 40–70 in assumed PLA, a wire-like arm 0.13, and no useful width fits a 220 mm bed whole (one arm with its hub does). Stylised, assumed material constants.
How should a laundry robot pick one garment out of a tangled dryer load?How should a laundry robot pick one garment out of a tangled dryer load? In a stylised disc-pile model aiming at a visible opening takes 13.4 assumed seconds per garment against 22.7 for pinching the top and regrasping, and pushing garments aside first is slower (17.7 s); one arm then serves 151, 110 and 127 items an hour. Assumed times, not a robot.
How long may a laundry robot spend finding a garment's opening before blind pinching is faster?How long may a laundry robot spend finding a garment's opening before blind pinching is faster? On paired piles of the stylised pick model, aiming wins only if the detector costs under about 9 s per pick (95% interval 8.9 to 9.6, assumed times); the limit falls as regrasps get likelier to succeed or more openings are hidden. Stylised.
How often may a laundry robot's opening detector aim wrong before blind pinching is faster?How often may a laundry robot's opening detector aim wrong before blind pinching is faster? In the stylised hanger-pick model each 10 points of detector error cost about 1 s of look-time budget: 9.2 s per pick when perfect, 4.5 s at 50% wrong, none when always wrong. Stylised.
Does the 2D hanger result survive in 3D, with front and back panels and a hanger arm of finite depth?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.
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.

All 25 Laundry-hanging robots projects →

Other research topics

Proper scoring & elicitation28 Effort & peer prediction9 Verification games & slashing19 Detection & tolerances14 Markets as routers13 Prediction-market design9 Paying contributors8 Decentralised training systems23 Digital twins & sim-to-real164 LLM chip design loops40 Robot manufacturing loops53 Ambient AI assistant46 Self-replicating manufacturing34 Simulation-first design64 Engineered clothing45 Task-specific AI accelerators40 Algorithmic chip-design loops42 Open-source purpose-built robots44 The software-engineering loop43 Adversarial human imitation41 Open-source residential construction41 Autonomous multi-angle capture34 Sensing, imaging and 3D31 Energy supply and dense energy storage39 Robot societies among people31 Agentic co-design of low-cost robots27 Laundry-hanging robots25 Machine learning on qualitative data23 Robots on the building site27 Civilizations & group psychology1 History & 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.