An adapted belief-space iLQR that penalizes parameter uncertainty outperforms regression and filtering baselines for simultaneous system identification and control in partially observable domains.
Model identification ada p- tive control: implementation case studies for a high manoeu vrability aircraft,
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Model Identification Adaptive Control with $\rho$-POMDP Planning
An adapted belief-space iLQR that penalizes parameter uncertainty outperforms regression and filtering baselines for simultaneous system identification and control in partially observable domains.