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Creators/Authors contains: "Pan, Zhao"

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  1. ; ; ; ; ; (, Experiments in Fluids)
    Free, publicly-accessible full text available May 1, 2026
  2. ; ; ; (, Experiments in Fluids)
    Full Text Available 
  3. (, IEEE robotics automation letters)
    Tamim Asfour, editor in (Ed.)
    A reinforcement learning (RL) control policy could fail in a new/perturbed environment that is different from the training environment, due to the presence of dynamic variations. For controlling systems with continuous state and action spaces, we propose an add-on approach to robustifying a pre-trained RL policy by augmenting it with an L1 adaptive controller (L1AC). Leveraging the capability of an L1AC for fast estimation and active ompensation of dynamic variations, the proposed approach can improve the robustness of an RL policy which is trained either in a simulator or in the real world without consideration of a broad class of dynamic variations. Numerical and real-world experiments empirically demonstrate the efficacy of the proposed approach in robustifying RL policies trained using both model-free and modelbased methods. 
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    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, IEEE Conference on Decision and Control)
    null (Ed.)
    Accepted Manuscript Full Text Available