Which three-finger pad design picks the most unbruised strawberry-like fruit under a 60 USD cap, and does a session agent beat CMA-ES and Bayesian optimisation?

What it found

Five design variables (pad width, pad length, pad modulus, grip force, finger count) are scored on 600 simulated fruit with ripeness-dependent bruise thresholds, wet and dry friction and placement error, using closed-form line-contact pressure, friction hold and a capture-and-clutter law. At 40 evaluations the median best objective is 0.635 for Bayesian optimisation, 0.618 for a session agent’s hand-written proposals (one run, by the model’s own author), 0.611 for CMA-ES and 0.574 for random search; all 20 Bayesian-optimisation seeds matched or beat the agent. The best design (25 mm pads, 0.19 MPa, 8 N, three fingers) picks 74% clean; about 20% of fruit are lost to clutter collisions that no pad setting can change. Stylised, with assumed constants and no learned policy.

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

• Which suction gripper picks the most of a stylised mixed tote under an 80 USD cap, and does a session agent beat CMA-ES and Bayesian optimisation?Which suction gripper picks the most of a stylised mixed tote (boxes, polybags, bottles) under an 80 USD cap, and can an agent beat CMA-ES and Bayesian optimisation at 40 evaluations? The agent's best J was 0.519 against 0.478 for Bayesian optimisation (median of 20 seeds); the best design is two 18.5 mm cups at 80 kPa and 60 L/min, and a catalogue-default single 30 mm cup scores 0.075. Stylised, assumed constants.
• 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).
• 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.

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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.