Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter

2025-08-25

AI Summary

This study investigates how synthetic active particles can adapt to hidden hydrodynamic disturbances without explicit sensing mechanisms, relying solely on physical embodiment. Method: We conceptualize the particle’s intrinsic physical dynamics as an implicit sensing channel, leveraging environment-coupled responses to encode unobserved flow-field information. Integrating reinforcement learning with a self-thermophoretic propulsion model, we optimize dynamic feedback policies for navigation in complex, non-uniform flow fields via simulation. Contribution/Results: The particles autonomously resist unknown flow perturbations and achieve robust navigation—outperforming conventional state-input-based perception-decision paradigms. Our work demonstrates that embodied dynamics inherently support information processing, revealing a principled mechanism for sensor-free adaptation. It establishes a novel theoretical framework and implementation pathway for microscale soft robots, ultra-low-power biologically inspired computing, and sensing-free intelligent agents.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Synthetic active particles adapt to hidden hydrodynamic perturbations without sensors
• Physical embodiment enables implicit sensing beyond explicit state inputs
• Embodied dynamics serve as computational resource for information processing

Innovation

• Physical embodiment enables implicit sensing without explicit mechanisms
• Reinforcement learning controls self-thermophoretic particles for navigation
• Embodied dynamics serve as computational resource for information processing