Generalizable Output Feedback Optimal Control via Transformers Without Explicit State Estimation
Overview
This paper presents a transformer-based policy capable of controlling a family of partially observed, noisy linear time-invariant systems without requiring explicit state estimation or plant matrices. Trained to imitate optimal Linear Quadratic Gaussian (LQG) control across heterogeneous MIMO systems with varying dimensions and noise levels, the approach uses shared representations with standardization and masking. At runtime, the policy generates control actions from a short window of noisy measurements. The study characterizes the relationship between the optimal LQG policy and the static transformer, expressing closed-loop excess cost as the energy of deviation from optimal action. Evaluation on 28 simulated systems shows the trained policy reaches optimal costs within a median gap of 0.4% for seen systems, with fine-tuning reducing the median gap to 1.0% for unseen systems.
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Environment: simulation
“d no state estimator. We characterize the relation between the optimal LQG policy (represented as a dynamic state-space model) and a static transformer acting on a finite window of measurements. We then express the closed-loop excess cost of any causal policy as the energy of its deviation from the optimal action. Finally, we evaluate the approach on 28 simulated systems. The trained policy reaches the optimal cost t”
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- arXiv Robotics — research abstractsSignalGeneralizable Output Feedback Optimal Control via Transformers Without Explicit State Estimation
This paper presents a transformer-based policy capable of controlling a family of partially observed, noisy linear time-invariant systems without requiring explicit state estimation or plant matrices. Trained to imitate optimal Linear Quadratic Gaussian (LQG) control across heterogeneous MIMO systems with varying dimensions and noise levels, the approach uses shared representations with standardization and masking. At runtime, the policy generates control actions from a short window of noisy measurements. The study characterizes the relationship between the optimal LQG policy and the static transformer, expressing closed-loop excess cost as the energy of deviation from optimal action. Evaluation on 28 simulated systems shows the trained policy reaches optimal costs within a median gap of 0.4% for seen systems, with fine-tuning reducing the median gap to 1.0% for unseen systems.
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