Which printable two-finger gripper holds eight different objects for the least plastic, and can an agent beat CMA-ES and Bayesian optimisation at 40 simulations?
research/codesign-gripper
Which printable two-finger gripper holds eight objects for the least plastic, and can an agent beat CMA-ES and Bayesian optimisation at 40 MuJoCo evaluations? Median best objective 0.991 (Bayesian opt) vs 0.979 (CMA-ES), 0.965 (random) and 0.963 (session agent, one run); the cheapest winners (0.5 g flat fingers) are also the most fragile when object offsets triple. Stylised, no learned policy.
What it found
A parametric 3D-printable gripper finger (length, thickness, pad width, V angle) scored in MuJoCo on eight objects with a per-design grip force. At 40 evaluations the median best objective is 0.991 for Bayesian optimisation, 0.979 for CMA-ES, 0.965 for random search and 0.963 for a session agent’s hand-written proposals (one run; Bayesian optimisation matched or beat it in 10 of 10 seeds). The best designs are tiny flat fingers (0.5 g), but re-scored with object offsets tripled they hold 5 of 8 objects against 6 of 8 for the agent’s wide V. Rigid, stylised, no learned policy, nothing printed.
A figure from its own output
Paper, code and results
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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.