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Holonic Digital Twins: From Passive Mirrors to Active Agents for Physical AI
US & West ·
The paper addresses AI's limitations in physical systems like robots and vehicles, which fail in long-horizon planning under real-world uncertainties. It proposes wireless networks to orchestrate 'physical intelligence' by maintaining shared spatiotemporal context, introducing holonic digital twins as active agents rather than passive mirrors.
Why it matters
It shows digital twins evolving into active agents for coordinating dynamic distributed systems, relevant for managing complex physical networks.
The paper establishes deterministic feasibility guarantees for differentiable predictive control (DPC), a self-supervised approach for approximating explicit MPC policies. Through topological analysis of the induced reachable safe set, it shows DPC can guarantee feasibility without online safety filters.
A data-driven reachability analysis method computes over-approximations of reachable sets directly from online state measurements, without requiring an accurate dynamic model. It estimates time-varying unknown models using an exponentially forgetting zonotopic recursive least squares method that handles bounded noise.
The company Aktiv published the final document of a foresight session on the future of cyber-physical systems security in Russia. The document provides strategic recommendations and industry forecasts up to 2040.
