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Found 5 papers, 0 papers with code
Safe Importance Sampling in Model Predictive Path Integral Control
We introduce the notion of importance sampling under embedded barrier state control, titled Safety Controlled Model Predictive Path Integral Control (SC-MPPI).
Gaussian Process Barrier States for Safe Trajectory Optimization and Control
We derive the barrier state dynamics utilizing the GP posterior, which is used to construct a safety embedded Gaussian process dynamical model (GPDM).
Safety in Augmented Importance Sampling: Performance Bounds for Robust MPPI
This work explores the nature of augmented importance sampling in safety-constrained model predictive control problems.
Robustifying Reinforcement Learning Policies with $\mathcal{L}_1$ Adaptive Control
A reinforcement learning (RL) policy trained in a nominal environment could fail in a new/perturbed environment due to the existence of dynamic variations.
Propagating Uncertainty through the tanh Function with Application to Reservoir Computing
In this work, we contribute two new methods to propagate uncertainty through the tanh activation function and propose the Probabilistic Echo State Network (PESN), a method that is shown to have better average performance than deterministic Echo State Networks given the random initialization of reservoir states.
