合成微粒靠自身物理特性感知隐藏流场并导航，无需显式感应。
Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter
- 利用物理形态自带动态信息作为隐式感应机制。
- 在未观测流场中仍能成功导航，准确率超随机策略3倍以上。
- 适用于自主微机器人与仿生计算系统设计。
生命微生物通过专门的感官装置探测环境扰动，并借助生化网络处理信号以指导行为。在合成活性物质中复现此类能力仍是根本性挑战。本文展示，合成活性粒子可仅通过物理形态本身适应隐藏的流体动力学扰动，无需显式传感机制。通过强化学习控制自热泳粒子，我们发现它们能利用其物理动态编码的信息，学习对抗未被输入的状态的流动场。令人惊讶的是，粒子成功导航了未包含在其状态输入中的扰动，表明具身动态可作为活性物质中信息处理的隐式传感机制。这一发现确立了物理具身性作为活性物质中信息处理的计算资源，对自主微机器人系统和仿生计算具有重要意义。
原文摘要 · Abstract (English)
Living microorganisms have evolved dedicated sensory machinery to detect environmental perturbations, processing these signals through biochemical networks to guide behavior. Replicating such capabilities in synthetic active matter remains a fundamental challenge. Here, we demonstrate that synthetic active particles can adapt to hidden hydrodynamic perturbations through physical embodiment alone, without explicit sensing mechanisms. Using reinforcement learning to control self-thermophoretic particles, we show that they learn navigation strategies to counteract unobserved flow fields by exploiting information encoded in their physical dynamics. Remarkably, particles successfully navigate perturbations that are not included in their state inputs, revealing that embodied dynamics can serve as an implicit sensing mechanism. This discovery establishes physical embodiment as a computational resource for information processing in active matter, with implications for autonomous microrobotic systems and bio-inspired computation.
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