EvoGymCM: Harnessing Continuous Material Stiffness for Soft Robot Co-Design
2026-04-09 • Robotics
Robotics
AI summaryⓘ
The authors developed EvoGymCM, a new tool for designing soft robots that treats material stiffness as a continuous factor instead of just fixed steps. This allows robots to better adjust their shape, material stiffness, and control to different tasks. They created two versions: one for materials that can change stiffness dynamically, and one for materials with fixed stiffness. Through experiments, the authors showed that optimizing material stiffness continuously helps robots perform better by coordinating their shape, material, and control together.
soft robotsmaterial stiffnessco-designmorphologyrobot controlprogrammable materialsoptimizationEvoGymcontinuous design space
Authors
Le Shen, Kangyao Huang, Wentao Zhao, Huaping Liu
Abstract
In the automated co-design of soft robots, precisely adapting the material stiffness field to task environments is crucial for unlocking their full physical potential. However, mainstream platforms (e.g., EvoGym) strictly discretize the material dimension, artificially restricting the design space and performance of soft robots. To address this, we propose EvoGymCM (EvoGym with Continuous Materials), a benchmark suite formally establishing continuous material stiffness as a first-class design variable alongside morphology and control. Aligning with real-world material mechanisms, EvoGymCM introduces two settings: (i) EvoGymCM-R (Reactive), motivated by programmable materials with dynamically tunable stiffness; and (ii) EvoGymCM-I (Invariant), motivated by traditional materials with invariant stiffness fields. To tackle the resulting high-dimensional coupling, we formulate two Morphology-Material-Control co-design paradigms: (i) Reactive-Material Co-Design, which learns real-time stiffness tuning policies to guide programmable materials; and (ii) Invariant-Material Co-Design, which jointly optimizes morphology and fixed material fields to guide traditional material fabrication. Systematic experiments across diverse tasks demonstrate that continuous material optimization boosts performance and unlocks synergy across morphology, material, and control.