Sample average approximation of CVaR-based hedging problem with a deep-learning solution

Cheng, Peng; Li, Shuang; Zhao, Yanlong; Bao, Ying

doi:10.1016/j.najef.2020.101325

Cited by 5 publications

(1 citation statement)

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“…Instead, it is typically computed by sampling from the risk variable of interest [37], or relies on assumptions of the underlying distribution. More recently, Peng et al [32] propose a method for predicting CVaR with a deep neural network under assumptions of i.i.d. samples, and prove convergence of the scheme.…”

Section: Introductionmentioning

confidence: 99%

Learning Risk-aware Costmaps for Traversability in Challenging Environments

Fan,

Dey,

Agha-mohammadi

et al. 2021

Preprint

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One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move. A significant source of difficulty in this determination arises from stochasticity and uncertainty, coming from localization error, sensor sparsity and noise, difficult-to-model robotground interactions, and disturbances to the motion of the vehicle. Classical approaches to this problem rely on geometric analysis of the surrounding terrain, which can be prone to modeling errors and can be computationally expensive. Moreover, modeling the distribution of uncertain traversability costs is a difficult task, compounded by the various error sources mentioned above. In this work, we take a principled learning approach to this problem. We introduce a neural network architecture for robustly learning the distribution of traversability costs. Because we are motivated by preserving the life of the robot, we tackle this learning problem from the perspective of learning tail-risks, i.e. the Conditional Value-at-Risk (CVaR). We show that this approach reliably learns the expected tail risk given a desired probability risk threshold between 0 and 1, producing a traversability costmap which is more robust to outliers, more accurately captures tail risks, and is more computationally efficient, when compared against baselines. We validate our method on data collected a legged robot navigating challenging, unstructured environments including an abandoned subway, limestone caves, and lava tube caves.

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Section: Introductionmentioning

confidence: 99%