论文/arXiv 2610.08541·22 天前

面向安全实时机器人控制的 Micro Neural Policies

针对受限嵌入式设备上的安全实时控制，结合ES与SMC验证搜索MNP，并在Cartpole、Quadrotor及物理系统上验证。

AI 整理

• 本体 SDK 与控制
• 仿真与训练
• 端侧部署

原文链接：Micro Neural Policies for Safe Real-Time Robotic Control（arXiv 2610.08541 · 发布日期：2026-10-06）

Micro Neural Policies for Safe Real-Time Robotic Control

针对受限嵌入式设备上的安全实时控制，结合ES与SMC验证搜索MNP，并在Cartpole、Quadrotor及物理系统上验证。

核心要点

• 目标是在计算受限嵌入式设备上实现安全、鲁棒的实时机器人控制。
• 将Evolution Strategy与SMC验证结合进行策略搜索，以显著缩小神经网络规模。
• 在Cartpole和Quadrotor任务上大规模训练评估，覆盖不同控制频率和网络结构。
• 仿真后零样本迁移到物理系统，并可在微控制器上实现实时推理与低抖动。

验证范围与局限：验证范围为Cartpole和Quadrotor控制任务，先在仿真中验证，再零样本迁移到物理系统。

英文摘要 (Abstract)

In this paper, we investigate the synthesis of Micro Neural Policies (MNP) to enable safe and robust real-time robotic control on computationally constrained embedded devices. We demonstrate that integrating Evolution Strategy (ES) and Statistical Model Checking (SMC)-based verification for policy search can drastically reduce neural network size without compromising safety and robustness. We conduct a large-scale training and evaluation of MNP on Cartpole and Quadrotor control tasks, varying control frequencies and network architectures. After validating these policies in simulation, we evaluate their deployability through zero-shot transfer to physical systems. Our experiments show that MNP can successfully achieve safe sim-to-real transfer without sacrificing control performance. We then show that the policies' memory footprint, ranging from 0.5 to 7.5 kB, allows deployment on microcontrollers, where they achieve real-time inference latency with under 25 ns of jitter while leaving the chip idle for over 97% of the time for additional workloads. This makes them a highly practical solution for severely resource-constrained robotic systems.

作者：Hongpeng Cao, Riccardo Curcio, Daniele Ottaviano, Marco Caccamo

来源

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1. arXiv 2610.08541：Micro Neural Policies for Safe Real-Time Robotic Control访问 2026-10-21