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Found 7 papers, 0 papers with code
Gaussian Processes with State-Dependent Noise for Stochastic Control
In the proposed formulation, the residual model uncertainty consists of a nonlinear function and state-dependent noise.
Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles
The estimators output the estimate of the tire-friction model as well as the uncertainty of the estimate, which expresses the confidence in the model for different driving regimes.
Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter
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.
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles
To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold.
Automated Controller Calibration by Kalman Filtering
The control parameter calibration is driven by a training objective, which encompasses specifications on the performance of the dynamical system.
Maximum Likelihood Methods for Inverse Learning of Optimal Controllers
This paper discusses theoretic properties of the learning methods and presents simulation results that highlight the advantages of using the maximum likelihood formulation for learning objective functions.
Constrained Inverse Optimal Control With Application to a Human Manipulation Task
This brief presents an inverse optimal control methodology and its application to training a predictive model of human motor control from a manipulation task.
