Spiking Neural Networks (SNNs) has the ability to extract spatio-temporal features due to their spiking sequence. While previous research has primarily foucus on the classification of image and reinforcement learning. In our paper, we put forward novel diffusion policy model based on Spiking Transformer Neural Networks and Denoising Diffusion Probabilistic Model (DDPM): Spiking Transformer Modulate Diffusion Policy Model (STMDP), a new brain-inspired model for generating robot action trajectories. In order to improve the performance of this model, we develop a novel decoder module: Spiking Modulate De coder (SMD), which replaces the traditional Decoder module within the Transformer architecture. Additionally, we explored the substitution of DDPM with Denoising Diffusion Implicit Models (DDIM) in our frame work. We conducted experiments across four robotic manipulation tasks and performed ablation studies on the modulate block. Our model consistently outperforms existing Transformer-based diffusion policy method. Especially in Can task, we achieved an improvement of 8%. The proposed STMDP method integrates SNNs, dffusion model and Transformer architecture, which offers new perspectives and promising directions for exploration in brain-inspired robotics.

翻译：脉冲神经网络（SNNs）凭借其脉冲序列特性，具备提取时空特征的能力。然而，先前的研究主要集中在图像分类和强化学习领域。本文提出了一种基于脉冲Transformer神经网络和去噪扩散概率模型（DDPM）的新型扩散策略模型：脉冲Transformer调制扩散策略模型（STMDP），这是一种用于生成机器人动作轨迹的新型类脑模型。为了提升该模型的性能，我们开发了一种新颖的解码器模块：脉冲调制解码器（SMD），用以替代Transformer架构中传统的解码器模块。此外，我们还在框架中探索了使用去噪扩散隐式模型（DDIM）替代DDPM的效果。我们在四个机器人操作任务上进行了实验，并对调制模块进行了消融研究。我们的模型在性能上持续超越现有的基于Transformer的扩散策略方法。特别是在Can任务中，我们实现了8%的性能提升。所提出的STMDP方法融合了SNNs、扩散模型和Transformer架构，为类脑机器人学领域的探索提供了新的视角和具有前景的研究方向。