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Autonomous multi-angle capture

Does the capture-kit packing result survive real, sourced device weights, energies and runtimes?

research/capture-realkit

What does the capture-kit packing result look like with real, sourced devices (Insta360 X4, DJI Osmo Action 4, iPhone 15 Pro Max, Sony a7C II and lenses, Anker packs)? Exact search reaches all 18 shots at 4 kg, greedy stalls at 12; the unsourced mount masses move the exact figure by up to 10 shots.

8 tests

What it found

Re-runs the capture-kit search (11,520 kits, 6,912 legal) on a catalogue of real devices whose weights, battery energies and runtimes come from manufacturer and retailer pages read 2026-10-21 (Insta360 X4, DJI Osmo Action 4, iPhone 15 Pro Max, Sony a7C II with three lenses, Peak Design tripod, two Anker banks). With a 4 h recording floor, exact search reaches all 18 band × viewpoint shots at 4 kg (the assumed catalogue needed 5 kg) and a greedy packer stalls at 12, as before: the lens-needs-a-body complementarity survives real figures. Sweeping the figures no source gave (phone recording draw, selfie-pole, mini-tripod and mast masses, within ±30%) shows the ranking is robust (greedy is below exact in 77–99% of draws from 1.5 kg up) but the exact variety at a given budget is not: at 4 kg it falls from 18 to as low as 8, so the mount masses, which no source gave, matter more than any camera. Not sourced: those mass and draw figures, the iPhone recording draw (Apple publishes playback only), cell voltages used to turn mAh into Wh, and which bands each device covers.

A figure from its own output

Figure produced by research/capture-realkit: kit
capture-realkit/experiments/kit.svg, written by the project's own experiment code.

Paper, code and results

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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.