用单模块数据控制多模块软体机器人，无需重新训练。
Module Number Adaptive Visual Shape Control for Serial Modular Soft Robots
- 将整体图像切分为模块局部区域，统一使用单模块控制器。
- 支持1到5个模块的机器人，在不同负载下仍能精准控形。
- 适合需要快速适配新结构的柔性机器人应用。
基于图像的形状控制为软体机器人全身构型控制提供了简便方法。然而，现有数据驱动方法通常针对固定结构设计，当模块数量变化时需重新收集控制数据。本文提出一种模块数自适应的视觉形状控制方法，适用于串联式模块化气动软体机器人。仅用单模块驱动数据训练的控制器，通过将整体相机图像分解为局部模块图像块，实现对1至5个模块机器人的控制。一个通用模块分割器可定位所有配置下的个体模块，同一局部控制器应用于每个提取的图像块。几何数据增强提升了向下游模块的迁移能力，轻量级掩码重建网络可合成恢复被移除的执行器掩码通道。物理实验表明，该方法在模块数变化、环境扰动和负载变化下均能实现有效形状控制。结果表明，仅需单模块控制学习即可实现无需特定配置数据的可扩展全身控制。
原文摘要 · Abstract (English)
Image based shape control provides a simple means of controlling the whole body configuration of soft robots. However, existing data driven approaches are typically developed for fixed robot structures and require new control data when the number of modules changes. This paper presents a module number adaptive visual shape control method for serial modular soft pneumatic robots. A controller trained only on single module actuation shape data is reused for robots with one to five modules by decomposing whole body camera images into local module patches. A single common module segmenter localizes individual modules across all tested configurations, while the same local controller is applied to every extracted patch. Geometric data augmentation improves transferability to downstream modules, and a lightweight mask reconstruction network reconstructs a synthetically removed actuator mask channel. Experiments on physical robots demonstrate shape control across varying numbers of modules and under environmental changes and payload loading. The results show that single module control learning enables scalable whole body control without configuration specific control data collection.
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