All Physical AI
Needs a Cerebellum
Physical AI has traditionally approximated the perceptual and reasoning capabilities of the cerebral cortex, leaving a critical sensory gap, relative to the human brain architecture, that makes safety precarious for operations beyond controlled environments.
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The consequence is a hard ceiling on machine autonomy. No amount of additional training data, compute, or architectural refinement closes the gap, because the gap is not one of scale, but of methodology.
Stratos Perception's patented Artificial Cerebellar Intelligence (ACI™)
With no end in sight, because they are untrusted with the road and passengers, robo-taxis continue to carry safety drivers. Likewise, humanoid robots have been dancing in videos and on stage for a decade, unable to be deployed safely into the stochastic real world of people.
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ACI™ is designed to change that. It provides physical AI systems a cerebellar companion that comprehensively infers physical conditions and attributes that are hidden to conventional sensors and estimators, with the potential to enable robots and automated systems to reach their promised potential in the stochastic real world.
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Finally.
Where Conventional Estimation Ends, ACI™ Begins
Estimates any number of independently observable time-varying parameters, including when their quantity far exceeds what conventional mathematics can uniquely solve
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Can provide 100+ distinct parameter estimates from a single sensed system output
Operates on severely underconstrained nonlinear dynamical systems where conventional estimators diverge or fail
Trained entirely on synthetic data, with no failure history or instrumented testbed required
Estimates parameters and states in real time at control system frequencies
For currently controlled systems, requires no additional sensors or hardware modifications
Applicable to any system representable by a reduced order computational model
Where are the Level 3+ Digital Twins?
Level 3+ digital twins are discussed liberally in literature and elsewhere. However, beyond academic interests, level 3 digital twins do not exist for complex machines and systems.
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Despite the significant efforts of engineers and scientists at national laboratories and universities, level 3 digital twins have been substantially barred from being realized for complex systems due to physical and mathematical realities that have prohibited accurate real-time estimation of many internal hidden time-varying attributes of a complex system. Without such estimation capabilities, "twins" are just remote displays, with scarce capability to provide insight into stochastic and novel behaviors and disturbances.
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ACI™ has overcome such barriers. It utilizes the readily available inputs and outputs of a system or environment to perform accurate real-time estimation of hundreds of hidden physical attributes and stochastic behaviors that are otherwise impractical or impossible to measure.
Further, ACI™ is trained using only reduced-order simulation data. It does not require new sensor hardware or extensive failure histories from high-value systems, and can perform comprehensive physical attribute estimation and situational awareness at approximately a thousandth of the market-typical start-up and implementation costs.
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ACI™ Applications
ACI™ provides control systems comprehensive coverage of physical attributes, disturbances, and configurations concerning time-varying systems and environments.
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Current and target applications include:
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Complex Dynamical Threat Environment Situational Awareness for Satellites and Submarines
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Failure Avoidance and Fault-Tolerance Capabilities in Aircraft and Spacecraft
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Auto-Tuning Control Systems Concerning Manufacturing Tolerances and System Maintenance
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Life-Cycle Management of Distributed Machines and Robots
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Robot Swarm Management and Autonomous Planning and Reconfiguration
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High Fidelity Performance Digital Twinning
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Autonomous Process Control
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Relative to the State of The Art
There are many parameter estimation methods in practice, including frequent innovations in the literature.
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However, all such methods are mathematically limited by a maximum number of parameter estimates, not including state estimates, that is roughly equal to the number of independent output signals used to derive the estimates.
Such parameters are physical attributes and disturbances, including system degradations, engineering deviations, and environmental disturbances.
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ACI™ overcomes this traditional limit, accurately inferring essentially any number of time-varying parameters from scarce output signal resources, potentially providing one hundred or more distinct parameter estimates from the monitoring of a single system output.
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Control of the Most Difficult Systems
Few engineering systems demand more from adaptive control than a tokamak fusion reactor. Stratos Perception is investigating the application of ACI™ to this challenge.
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As fusion energy approaches engineering viability, plasma control has emerged as one of its most formidable unsolved problems. The plasma state is governed by a large number of hidden, time-varying parameters and unmeasured internal states, a problem class where ACI™ is purpose-built to operate.
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Conventional approaches rely on Extended Kalman Filtering for state and parameter estimation. These methods perform adequately under relatively stable conditions, but break down under transient events, precisely the scenarios where control failures carry the most severe physical and economic consequences.
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ACI™'s real-time inferential architecture is designed for exactly this environment: high-dimensional, under-constrained, and unforgiving of error.
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The stakes are commensurate with the difficulty. Commercially viable fusion represents a multi-trillion dollar energy opportunity and a path to clean, virtually limitless power. It is among the most consequential applications we are pursuing.
