Despite recent progress in training spiking neural networks (SNNs) for classification, their application to continuous motor control remains limited. Here, we demonstrate that fully spiking architectures can be trained end-to-end to control robotic arms with multiple degrees of freedom in continuous environments. Our predictive-control framework combines Leaky Integrate-and-Fire dynamics with surrogate gradients, jointly optimizing a forward model for dynamics prediction and a policy network for goal-directed action. We evaluate this approach on both a planar 2D reaching task and a simulated 6-DOF Franka Emika Panda robot with torque control. In direct comparison to non-spiking recurrent baselines trained under the same predictive-control pipeline, the proposed SNN achieves comparable task performance while using substantially fewer parameters. An extensive ablation study highlights the role of initialization, learnable time constants, adaptive thresholds, and latent-space compression as key contributors to stable training and effective control. Together, these findings establish spiking neural networks as a viable and scalable substrate for high-dimensional continuous control, while emphasizing the importance of principled architectural and training design.

翻译：尽管脉冲神经网络在分类任务训练方面已取得进展，但其在连续运动控制中的应用仍显不足。本文证明，完全脉冲架构可通过端到端训练实现连续环境中多自由度机械臂的控制。我们的预测控制框架将漏积分发放动力学与代理梯度相结合，联合优化用于动力学预测的前向模型和用于目标导向动作的策略网络。该方法在平面二维到达任务和具有扭矩控制的模拟六自由度Franka Emika Panda机器人上进行了评估。在与相同预测控制流程训练的非脉冲循环基线模型直接对比中，所提出的脉冲神经网络在任务性能相当的情况下显著减少了参数量。深入的消融实验揭示了初始化策略、可学习时间常数、自适应阈值和潜空间压缩作为稳定训练与有效控制的关键要素。这些发现共同确立了脉冲神经网络作为高维连续控制可行且可扩展的基元，同时强调了系统性架构设计与训练策略的重要性。