Does a small neural roll controller beat the three-gain PID on a randomised quadrotor, and does it change which airframe is best?
research/codesign-aero-mlp
Does a 31-parameter neural roll controller beat the three-gain PID on a randomised quadrotor? Not at equal evaluations (7.7° PID against 9.0°); with 10× the evaluations it only matches it (7.6°), and the airframe ranking is unchanged (Spearman 1.00).
What it found
Follow-up to codesign-aero-robust: a 6-unit tanh network (31 parameters) trained by CMA-ES replaces the three-gain PID roll law on six airframes spanning the wide-plant ranking, three seeds each, scored on 64 held-out wide-plant episodes. At the same 96 evaluations the network is worse on 5 of 6 airframes (mean 8.96° against 7.72°); at 960 evaluations it matches the PID (7.59°, gains of 0.00 to 0.09° on five airframes and 0.53° on one). The best airframe and the ranking (Spearman 1.00) do not change, so the three-gain law did not distort the earlier sizing result. Limits: evolution strategies rather than PPO, no skip path, six airframes, roll only; the plant and randomisation ranges are assumed round numbers carried over from codesign-aero, not measurements.
A figure from its own output
Paper, code and results
More in Agentic co-design of low-cost robots
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- Do 24 runs per method rank gripper co-design methods that 8 runs could not?Do more seeds separate the gripper co-design methods? Not yet: with 24 runs per method CMA-ES leads random search by 0.025 (interval -0.007 to +0.056), the rollout-log reader trails by 0.022, and a 0.02 gap would need about 140 runs per method
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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.