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- W4385656276 abstract "Achieving highly accurate dynamic or simulator models that are close to the real robot can facilitate model-based controls (e.g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e.g., trajectory optimization), and decrease the amount of learning time necessary for reinforcement learning methods. Thus, the objective of this work is to learn the residual errors between a dynamic and/or simulator model and the real robot. This is achieved using a neural network, where the parameters of a neural network are updated through an Unscented Kalman Filter (UKF) formulation. Using this method, we model these residual errors with only small amounts of data — a necessity as we improve the simulator/dynamic model by learning directly from real-world operation. We demonstrate our method on robotic hardware (e.g., manipulator arm, and a wheeled robot), and show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware." @default.
- W4385656276 created "2023-08-09" @default.
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- W4385656276 date "2023-06-25" @default.
- W4385656276 modified "2023-09-25" @default.
- W4385656276 title "Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-Based Unscented Kalman Filter" @default.
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- W4385656276 doi "https://doi.org/10.1109/ur57808.2023.10202521" @default.
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