Does the best quadrotor airframe stay best when motor strength, mass and motor lag are randomised?

What it found

Robustness follow-up to codesign-aero: 50 feasible airframes, each with roll gains tuned on the nominal, a narrow or a wide randomised plant (torque 0.7–1.0×, inertia 0.9–1.3×, motor lag 1–2×, assumed). With nominal-tuned gains the nominal and wide rankings have Spearman 0.75 and the same top five; the nominal optimum is 2nd of 50 on the wide plant, 0.17° behind the best. Tuning gains on the wide plant helps the median airframe by 0.91°. One tuning run per cell, so neighbouring ranks are within noise. Stylised, roll axis only, simulation only.

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

• Should a quadrotor’s airframe and controller gains be searched jointly when the plant is uncertain?Should airframe and gains be searched jointly under an uncertain plant? Yes in this stylised model: joint CMA-ES at 960 evaluations (5.75° median held-out, 8 seeds) beats random search and a 4800-evaluation grid-then-tune best cell (6.00°).
• Is the quadrotor airframe ranking from one tuning run reproducible, or is it tuning noise?Is the airframe ranking from one tuning run reproducible? With wide-tuned gains yes (Spearman 0.99 over six replicates); with nominal-tuned gains it is noisier (0.84) and the earlier 0.17° regret was within noise (stylised).
• 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).

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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.