Is the quadrotor airframe ranking from one tuning run reproducible, or is it tuning noise?

What it found

Seed-repeat follow-up to codesign-aero-robust: the 50-airframe grid is re-tuned and re-scored six times with independent CMA-ES, training and held-out seeds. With gains tuned on the wide randomised plant the ranking is highly reproducible (Spearman 0.99 between replicates, same top five every time); with nominal-tuned gains it is noisier (0.84, winner varies in arm length), so the earlier “rank 2, 0.17° regret” was inside the noise. Retuning for the wide plant helps the median airframe by 0.88° on average. Stylised (assumed constants, roll axis only, six replicates).

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Agentic co-design of low-cost robots

• Does the ranking of printable soft-fruit gripper designs survive a shift in friction, fruit, placement error and print tolerance?Does the ranking of soft-fruit gripper designs survive a shift in friction, fruit, placement error and print tolerance? Yes in this model (Spearman 0.993 over 600 designs), but the nominal optimum loses 13% of its J and a robust optimum gains only about 0.015 on average; sim-to-sim, assumed ranges.
• Which grasp scenes can no printable two-finger gripper hold, and what limits the ceiling?Which grasp scenes can no printable two-finger gripper hold? 10 of 140 (7%), all spheres with the hand more than about 2 mm above their centre height; lowering only the hand holds all 10, so the 0.85 plateau in codesign-diagnose is scene geometry, not friction or force (stylised, found after the fact, one intervention).
• Does diagnostic feedback (a failure summary, full rollout logs) help a design loop find better printable grippers than the score alone?Does a failure summary or full rollout logs help a gripper design loop beyond the score alone? No: three scripted readers end at 0.84 to 0.86 best test success, like random search, CMA-ES and Bayesian optimisation (stylised, rule-based readers, not an LLM).
• How much of a best-of-40 gripper design's score is selection noise when every design gets its own trained grasp force?Do agent-proposed gripper fingers beat random search, CMA-ES and Bayesian optimisation when each design gets its own trained grasp force? On 40 held-out scenes all methods' best seeds reach 0.93 to 0.95 and the ranking is within noise; on 100 fresh scenes the session agent's 0.95 falls to 0.81, level with the baseline median (stylised, preliminary, mostly negative).
• Does tilting a quadrotor's rotors buy the yaw authority that no attitude controller could?Does a rotor cant angle, co-designed with arm length and propeller size, beat the yaw limit that control-law changes could not move? In simulation yes: joint co-design with free cant (chosen at the 35° bound in 5 of 5 seeds) cuts held-out attitude error from 15.22° to 12.32°, at equal evaluations.
• Where is the best rotor cant angle for a quadrotor co-designed with its controller?Does more rotor tilt keep helping once the 35° cap of codesign-aero-cant is lifted? In simulation no: with the bound at 35°, 50°, 65° and 80° the joint co-design reaches 12.3° held-out attitude error and picks 34–40° each time, so the optimum is interior and flat; an assumed tilt efficiency loss changes it by 0.1°.

All 27 Agentic co-design of low-cost 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.