The Morphology-Control Trade-Off: Insights into Soft Robotic Efficiency

TL;DR

This paper tackles the problem of understanding how morphology and control complexity interact to determine task performance in soft robots. It introduces an integrated framework that uses four morphology metrics and FLOPs to quantify control cost, combined with MAP-Elites and PPO in EvoGym. Regression analyses reveal task-dependent trade-offs, showing that easy tasks benefit from simpler morphologies and controllers while hard tasks require higher complexity in both dimensions, with a measurable trade-off front captured by $y=\beta_0+\beta_1 x_1+\beta_2 x_2$. The work provides actionable guidance for task-specific co-design of soft robots, emphasizing efficient computation and adaptability for real-world applications.

Abstract

Soft robotics holds transformative potential for enabling adaptive and adaptable systems in dynamic environments. However, the interplay between morphological and control complexities and their collective impact on task performance remains poorly understood. Therefore, in this study, we investigate these trade-offs across tasks of differing difficulty levels using four well-used morphological complexity metrics and control complexity measured by FLOPs. We investigate how these factors jointly influence task performance by utilizing the evolutionary robot experiments. Results show that optimal performance depends on the alignment between morphology and control: simpler morphologies and lightweight controllers suffice for easier tasks, while harder tasks demand higher complexities in both dimensions. In addition, a clear trade-off between morphological and control complexities that achieve the same task performance can be observed. Moreover, we also propose a sensitivity analysis to expose the task-specific contributions of individual morphological metrics. Our study establishes a framework for investigating the relationships between morphology, control, and task performance, advancing the development of task-specific robotic designs that balance computational efficiency with adaptability. This study contributes to the practical application of soft robotics in real-world scenarios by providing actionable insights.

Figures (4)

• Figure 1: An example of a soft robot with a $(5 \times 5)$ grid size in EvoGym.
• Figure 2: Exploration Framework for Morphology and Control Complexity.
• Figure 3: Scatter plots illustrating the trade-off between average morphological complexity and control complexity (FLOPs) across three tasks using the PPO controller.
• Figure 4: Sensitivity analysis of FLOPs and fitness to morphological complexity metrics across tasks. Each metric—voxel heterogeneity, structural connectivity, symmetry analysis, and actuator distribution—is represented by distinct colors. Vertical dashed lines separate the metrics for clarity, and distributions highlight variability, median, and extreme values.