Risk-Averse Zero-Order Trajectory Optimization
Abstract
We introduce a simple but effective method for managing risk in zero-order trajectory optimization that involves probabilistic safety constraints and balancing of optimism in the face of epistemic uncertainty and pessimism in the face of aleatoric uncertainty of an ensemble of stochastic neural networks. Various experiments indicate that the separation of uncertainties is essential to performing well with data-driven MPC approaches in uncertain and safety-critical control environments.
Cite
Text
Vlastelica et al. "Risk-Averse Zero-Order Trajectory Optimization." Conference on Robot Learning, 2021.Markdown
[Vlastelica et al. "Risk-Averse Zero-Order Trajectory Optimization." Conference on Robot Learning, 2021.](https://mlanthology.org/corl/2021/vlastelica2021corl-riskaverse/)BibTeX
@inproceedings{vlastelica2021corl-riskaverse,
title = {{Risk-Averse Zero-Order Trajectory Optimization}},
author = {Vlastelica, Marin and Blaes, Sebastian and Pinneri, Cristina and Martius, Georg},
booktitle = {Conference on Robot Learning},
year = {2021},
pages = {444-454},
volume = {164},
url = {https://mlanthology.org/corl/2021/vlastelica2021corl-riskaverse/}
}