用分段状态机控制6-16条腿机器人，在崎岖地形上实现自适应行走。
Walking on Rough Terrain with Any Number of Legs
- 每两腿配3个执行器，分段状态机逐段传递信号，实现模块化控制。
- 仿真验证6至16条腿的机器人均能稳定行走，接触地面时耦合反馈，无接触时产生虚幻运动。
- 轻量高效，适合作为强化学习控制器的基线或实际部署的自适应方案。
机器人若能复现节肢动物在复杂环境中的敏捷性将获益良多。本文研究具有6条及以上腿的多足系统控制问题。现有方法包括大型黑箱机器学习模型、中枢模式发生器（CPG）网络以及依赖机械稳定的开环前馈控制。本文提出一种用于崎岖地形的多足控制架构：采用每两腿配置3个执行器的分段式机器人，通过仿真验证了6至16条腿系统的可行性。各分段采用相同状态机，且接收前序分段输入。该设计融合了类似WalkNet的事件级联控制器与基于CPG的控制器优势：接触地面时紧密耦合，无接触时生成虚构运动。该方法可作为多足机器人轻量级自适应控制器，也可作为机器学习控制器的训练基线。
原文摘要 · Abstract (English)
Robotics would gain by replicating the remarkable agility of arthropods in navigating complex environments. Here we consider the control of multi-legged systems which have 6 or more legs. Current multi-legged control strategies in robots include large black-box machine learning models, Central Pattern Generator (CPG) networks, and open-loop feed-forward control with stability arising from mechanics. Here we present a multi-legged control architecture for rough terrain using a segmental robot with 3 actuators for every 2 legs, which we validated in simulation for robots with 6 to 16 legs. Segments have identical state machines, and each segment also receives input from the segment in front of it. Our design bridges the gap between WalkNet-like event cascade controllers and CPG-based controllers: it tightly couples to the ground when contact is present, but produces fictive locomotion when ground contact is missing. The approach may be useful as an adaptive and computationally lightweight controller for multi-legged robots, and as a baseline capability for scaffolding the learning of machine learning controllers.
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