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Robots on the building site

Which tasks of building a wood-frame house hold the crew hours, and which can a stock humanoid even hold the load for?

research/buildbot-taskbook

Which house-building tasks hold the crew hours, and which can a stock humanoid hold the load for? An open task book of a 28x40 ft wood-frame house: 20 tasks with cycles, masses, tolerances and crew-hours, scored against the simulated Unitree G1 and H1 payload envelopes; fastening is 31% of the hours, and the H1 passes the payload test for 53-81% of them (minutes per unit are assumptions, swept).

10 tests

What it found

An open task book of one small house (28×40 ft, one storey, 2×6 at 16 in, from house-framing's takeoff): 20 tasks across wall framing, wall and roof sheathing, insulation and drywall, each with cycles, mass handled, hand height, reach, a tolerance, minutes per unit and crew-hours, as CSV and JSON. Counts come from code (210 framing pieces, 32 wall and 46 roof sheets, 72 drywall sheets, 8,760 fasteners); masses from the lumber, sheet and tool figures of buildbot-payload; robot verdicts from its simulated G1 and H1 payload envelopes. Minutes per unit are my assumptions: no primary time study could be read from this session, so they are swept ×1/3 to ×3, and my framing rows come out 5.5× below the one cited norm I found (Methvin, 0.12 man-hours per metre of stud, read as a search snippet), shown as a second scenario. Total 46 crew-hours (40–77 over the sweep) for this scope, or 93 with framing at the norm. Fastening is the biggest verb (29% of the hours, 21% in the second scenario) and taping and sanding drywall the biggest task. The H1 passes the payload test for tasks holding 54–81% of the hours (57–85% in scenario B), the G1 only 6–22% (7–31%); roof and ceiling work sits outside both robots' reach envelope. A payload pass is not a skill: force, placement accuracy and perception are untested.

A figure from its own output

Figure produced by research/buildbot-taskbook: figure
buildbot-taskbook/experiments/figure.png, 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.