用脉冲神经网络生成机器人动作轨迹，更贴近大脑工作方式。
Brain-inspired Action Generation with Spiking Transformer Diffusion Policy Model
- 用脉冲变压器+扩散模型构建脑启发式动作生成框架
- 在4个任务中表现优于现有方法，罐子任务提升8%
- 适合研究类脑智能与机器人控制的学者
脉冲神经网络（SNN）因其脉冲序列能有效提取时空特征，被用于图像分类和强化学习。本文提出一种基于脉冲变压器神经网络与去噪扩散概率模型（DDPM）的新颖扩散策略模型——脉冲变压器调制扩散策略模型（STMDP），用于生成机器人动作轨迹。为提升性能，我们设计了新型解码模块：脉冲调制解码器（SMD），替代传统Transformer中的解码器。此外，探索了用去噪扩散隐式模型（DDIM）替代DDPM的可能性。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。结果表明，该模型始终优于现有基于Transformer的扩散策略方法，尤其在罐子任务中实现8%的性能提升。STMDP融合了SNN、扩散模型与Transformer架构，为类脑机器人研究提供了新思路与前景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。