发表机构
Politecnico di Milano; National Research Council（米兰理工大学; 国家研究委员会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种受生物认知启发的计算框架，通过神经储层编码和主动推理整合感知、行动与学习，使机械系统在部分可观察环境中自主适应，并在多案例模拟中验证其实现变形最大化与振动抑制等目标的潜力。
AI 中文摘要
机械系统传统上被设计为被动资产，其中感知、分析和控制被视为解耦且外部预设的过程。对预定义机械模型的依赖以及带有离线优化的监督学习进一步限制了它们在变化环境中的适应性。受生物认知的启发，我们引入了一个计算框架，使机械系统能够通过与部分可观察环境的交互来学习、适应和做出决策。感知、行动和学习通过感知-行动回路中的主动推理得以整合。传感器数据通过神经储层进行同化，以支持持续的信念更新，而行动则主动探测并影响物理系统，成为推理过程的一个组成部分。通过自主导向的交互，适应性行为从内部表征的持续细化中涌现，以解释变化的观测模式，同时追求机械目标。在涉及多个机械系统、静态和动态传感以及从变形最大化到振动抑制等目标的模拟案例研究中，我们展示了所提出框架作为自主机械系统使能范式的潜力。
英文摘要
Mechanical systems are traditionally designed as passive assets, where sensing, analysis, and control are treated as decoupled and externally prescribed processes. Reliance on predefined mechanistic models and supervised learning with offline optimization further limits their adaptability in changing environments. Drawing inspiration from biological cognition, we introduce a computational framework enabling mechanical systems to learn, adapt, and make decisions through interactions with partially observable environments. Perception, action, and learning are integrated through active inference within a perception-action loop. Sensor data are assimilated through a neural reservoir to support continual belief updating, while actions actively probe and influence the physical system, becoming an integral part of the inference process. Through self-directed interaction, adaptive behavior emerges from the continual refinement of internal representations to explain varying observation patterns while pursuing mechanical objectives. Across simulated case studies involving multiple mechanical systems, static and dynamic sensing, and objectives ranging from deformation maximization to vibration mitigation, we demonstrate the potential of the proposed framework as enabling paradigm for autonomous mechanical systems.
