Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter

TL;DR

Synthetic active particles can adapt to hidden environmental changes through physical embodiment alone, using their dynamics as an implicit sensing mechanism, without explicit sensors or biochemical networks.

Contribution

This work demonstrates that physical embodiment enables active particles to process information and adapt to unobserved perturbations, a novel approach in active matter control.

Findings

Particles learn navigation strategies via reinforcement learning.

Physical dynamics encode information about hidden flow fields.

Embodiment acts as an implicit sensing mechanism.

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. 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.