Which cheap wheel-legged rover covers the most stylised rubble under a 120 USD parts cap, and does a session agent beat CMA-ES and Bayesian optimisation?

What it found

Five design variables (wheel radius, wheelbase, flipper length, drive torque, battery) are scored on twelve held-out random-rubble terrains with Gamma-patchy friction, each design with its own evolution-strategies speed-and-flipper policy (not PPO). Wheel radius at its 90 mm bound with torque of about 0.5 N m reaches the ceiling J = 0.783 (16 of 20 m) at 69 USD. Median best J at 40 evaluations: Bayesian optimisation 0.782, CMA-ES 0.782, session agent 0.783, random search 0.716; the gaps at the top are within policy-search noise. Falls were zero throughout, so the tip-over and recovery path is untested. Stylised statics, assumed unsourced prices, one agent run with a calibration-time prior.

A figure from its own output

Paper, code and results

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

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• Should a quadrotor’s airframe and its learned attitude controller be sized together, or one after the other?Should a quadrotor's airframe and its learned roll controller be sized together? Yes in this stylised model: joint CMA-ES reaches 5.25° RMS roll error, equal to the grid optimum, against 7.06° for lightest-airframe-first; a controller reused across airframes is worse on 41% of them.
• Does the best quadrotor airframe stay best when motor strength, mass and motor lag are randomised?Does the best quadrotor airframe stay best under widened motor-strength, mass and lag randomisation? Mostly: top five unchanged (Spearman 0.75), but retuning gains helps the median airframe 0.91° (stylised).
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• 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).

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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.