EvoGymCM: Harnessing Continuous Material Stiffness for Soft Robot Co-Design

2026-04-09

AI Summary

Existing soft robot co-design platforms typically discretize material stiffness, thereby constraining the design space and limiting performance potential. This work proposes EvoGymCM, a benchmark suite that, for the first time, treats continuous material stiffness as a first-class design variable alongside morphology and control. It introduces two material paradigms—Reactive (programmable) and Invariant (conventional)—and develops corresponding co-design frameworks. By integrating reinforcement learning–based real-time stiffness modulation, joint optimization algorithms, and physically accurate simulation of continuous materials, the approach enables deep co-optimization across material, morphology, and control. Experimental results demonstrate significant performance improvements across diverse tasks, effectively unlocking synergistic interactions among these three design dimensions.

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.

Problem

• soft robot co-design
• material stiffness
• continuous materials
• design space limitation
• performance optimization

Innovation

• continuous material stiffness
• soft robot co-design
• programmable materials
• morphology-material-control coupling
• EvoGymCM