Physical embodiment enables information processing beyond explicit flow sensing in active matter

TL;DR

Microrobots can sense and adapt to hidden water flows using their physical structure, without needing special sensors.

Contribution

This study shows synthetic particles can use physical embodiment as an implicit sensing mechanism in active matter.

Findings

Synthetic particles navigate hidden flows without explicit sensing by using physical dynamics.

Reinforcement learning enables particles to adapt to unobserved flow fields.

Embodied dynamics serve as a computational resource for information processing in active matter.

Abstract

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. 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 bioinspired computation.

Microrobots use their bodies to sense and adapt to hidden flows, learning through embodiment.

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