Is it the motor or the battery that decides whether an exosuit pays for its own mass?
research/garment-assistpower
Adds a motor and battery mass model to the ankle exosuit: at an assumed 8 N m/kg the motor (about 8 kg) makes a gain-0.3 suit raise cost 0.2–1.9%; the sign flips between 8 and 20 N m/kg, battery hours matter little. Stylised; assumed constants.
What it found
Extends the ankle exosuit model with a power train: motor mass from peak torque and a torque density, battery mass from electrical energy and hours of use, and the resulting mass fed back into metabolic cost. With an assumed 8 N m/kg motor the gain-0.3 suit weighs about 9 kg, 8.4 of it motor, and raises whole-body cost 0.2–1.9%; the sign flips between 8 and 20 N m/kg, while eight hours of battery add under 2 kg. Stylised: every power-train constant is assumed, not sourced.
A figure from its own output
Paper, code and results
More in Engineered clothing
- Does it pay to move an exosuit's motor and battery from the shank to the waist?Moves the exosuit's motor and battery from shank to waist: the waist layout wins once a shank kg costs more than 1.03–1.39× a waist kg (assumed ratio, swept); at ratio 3 and 8 N m/kg shank +20.7% vs waist +3.3%. Stylised.
- How much cable bend can a waist-mounted exosuit power train tolerate before the shank layout wins?Replaces the assumed Bowden efficiency with the capstan law
exp(-mu*theta): at ratio 3 and mu 0.1 the waist layout beats the shank layout until the cable bends through about 31 rad, so friction loss alone does not decide the layout (hysteresis not modelled). Stylised. - What would a suit-like protective garment weigh and cost, and what limits it?Cunniff-style scaling for nylon, aramid and UHMWPE: at equal weight UHMWPE gives 1.49× aramid's V50 and needs 0.20–0.67× the areal density depending on an unknown exponent; a 3 mm, 1 kg suit-like panel falls short of a 4 kg/m² aramid reference except under optimistic assumptions. Stylised; recalled formula, assumed properties and prices, no protection claim.
- Which sensors in a smart garment recognise what you are doing, and how fragile is that to garment slip?Activity recognition from simulated chest/thigh/wrist IMUs and knee/elbow strain: two sensors reach ~99% and all five 99.9% on easy synthetic classes, but a clean-trained model falls to 45% under 1 rad garment slip (94% when trained with slip). Stylised; invented signals, no real dataset.
- Where does a designer coat's price go, and what does a $20 to $50 replica keep?Bottom-up cost model for a lined coat applied to two reported designer prices: modelled factory cost is under 15% of price, the assumed markup chain does not close the gap, and a $20–50 replica needs very cheap cloth and labour. All inputs assumed. Stylised.
- Can a cheap cloth drape score how well a flat pattern fits a body, and is a fitted pattern body-specific?Position-based cloth drape scores a flat skirt-tube pattern on two synthetic elliptical bodies by strain and waist ease; the best waist is near the body's waist perimeter and a pattern fitted to one body fits the other clearly worse. Stylised; strains depend on mesh and solver.
All 45 Engineered clothing 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.