用脉冲神经网络实现机器人连续控制，端到端训练效果媲美传统模型。
Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning
- 结合漏电积分-放电模型与代理梯度，端到端训练预测模型和策略网络。
- 在2D平面和6自由度机械臂任务中，性能接近非脉冲模型但参数更少。
- 可学习的时间常数和自适应阈值等设计对稳定训练至关重要。
尽管脉冲神经网络（SNNs）在分类任务上取得进展，但在连续运动控制中的应用仍有限。本文展示全脉冲架构可通过端到端建模训练，在连续环境中控制多自由度机械臂。所提预测控制框架结合漏电积分-放电动力学与代理梯度，联合优化动态预测前向模型与目标导向动作策略网络。在二维平面抓取任务和模拟的6自由度Franka Emika Panda机械臂扭矩控制任务上进行评估。与同框架下训练的非脉冲循环基线相比，该SNN达到相当的任务性能，但参数显著更少。大量消融实验表明，初始化、可学习时间常数、自适应阈值及潜在空间压缩是稳定训练与有效控制的关键因素。研究结果确立了脉冲神经网络作为高维连续控制可行且可扩展的载体，强调了合理架构与训练设计的重要性。
原文摘要 · Abstract (English)
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.
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