首个整合行走与手臂控制的脉冲神经网络系统
A Spiking Neural Architecture for Coordinating Arm and Locomotor Control
- 用脉冲神经网络结合基底节模型实现动作协调
- 在仿真中完成行走、绘图及任务切换
- 适合低功耗机器人控制研究者参考
脉冲神经网络（SNN）结合类脑硬件为类人机器人控制提供了节能方案。然而，现有基于SNN的运动控制系统分别处理双足行走和手臂控制，缺乏二者协同。本文提出一种基于神经工程框架（NEF）与语义指针架构（SPA）的脉冲神经架构，实现力控手臂与双足行走的联合控制。高层动作选择通过生物启发的脉冲基底节模型实现。通过Nengo与Isaac Sim的联合仿真验证，系统成功完成目标抓取、连续绘图、路径跟随行走，并能通过基底节去抑制实现行走与手臂控制间的切换。据我们所知，这是首个在全尺寸类人平台上实现双足行走与手臂控制集成的脉冲控制器。完整的脉冲实现为未来部署于低功耗类脑硬件提供了可能。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) coupled with neuromorphic hardware offer energy-efficient solutions for humanoid robot control. However, existing SNN-based motor control systems address bipedal locomotion and arm control in isolation, leaving integrated control of both unaddressed. We present a spiking architecture that coordinates force-based arm control and bipedal locomotion in a simulated humanoid, using the Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA). High-level action selection between locomotor and arm control is mediated by a biologically grounded spiking basal ganglia model. We validate the system through co-simulation of Nengo, for the neural control, and Isaac Sim, demonstrating successful target reaching, continuous digit drawing, path-following locomotion, and finally, switching between walking and arm control via basal ganglia disinhibition. To our knowledge, this is the first integrated spiking controller to combine bipedal locomotion and arm control on a full-scale humanoid platform. The full spike-based implementation enables future deployment on low-power neuromorphic hardware.
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