HYPOTHESIS AND THEORY article
Front. Robot. AI
Sec. Bio-Inspired Robotics
This article is part of the Research TopicNeuromorphic Engineering in wetware: achievements and perspectivesView all articles
Wetware Network-Based AI. A Chemical Approach to Embodied Cognition for Robotics and Artificial Intelligence
Provisionally accepted
- 1Libera Universita di Lingue e Comunicazione, Milan, Italy
- 2Universita degli Studi di Bologna, Bologna, Italy
- 3University of Salento, Lecce, Italy
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Wetware Network-Based Artificial Intelligence (WNAI) introduces a new approach to robotic cognition and artificial intelligence: autonomous cognitive agents built from synthetic chemical networks. Rooted in Wetware Neuromorphic Engineering, WNAI shifts the focus of this emerging field from disembodied computation and biological mimicry to reticular chemical self-organization as a substrate for cognition. At the theoretical level, WNAI integrates insights from network cybernetics, autopoietic theory and enaction to frame cognition as a materially grounded, emergent phenomenon. At the heuristic level, WNAI defines its role as complementary to existing leading approaches. On the one hand, it complements embodied AI and xenobotics by expanding the design space of artificial embodied cognition into fully synthetic domains. On the other hand, it engages in mutual exchange with neural network architectures, advancing cross-substrate principles of network-based cognition. At the technological level, WNAI offers a roadmap for implementing chemical neural networks and protocellular agents, with potential applications in robotic systems requiring minimal, adaptive, and substrate-sensitive intelligence. By situating wetware neuromorphic engineering within the broader landscape of robotics and AI, this article outlines a programmatic framework that highlights its potential to expand artificial cognition beyond silicon and biohybrid systems.
Keywords: Wetware Network-Based Artificial Intelligence (WNAI), wetware neuromorphicengineering, embodied AI, Chemical networks, Chemical robotics, minimal artificial agents, reticular self-organization, network cybernetics
Received: 28 Aug 2025; Accepted: 03 Dec 2025.
Copyright: © 2025 Damiano, Fleres, Roli and Stano. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Luisa Damiano
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