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arXiv · 2307.03167

Risk-Averse Trajectory Optimization via Sample Average Approximation

Thomas Lew·Riccardo Bonalli·Marco Pavone
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Abstract

Trajectory optimization under uncertainty underpins a wide range of applications in robotics. However, existing methods are limited in terms of reasoning about sources of epistemic and aleatoric uncertainty, space and time correlations, nonlinear dynamics, and non-convex constraints. In this work, we first introduce a continuous-time planning formulation with an average-value-at-risk constraint over the entire planning horizon. Then, we propose a sample-based approximation that unlocks an efficient and general-purpose algorithm for risk-averse trajectory optimization. We prove that the method is asymptotically optimal and derive finite-sample error bounds. Simulations demonstrate the high speed and reliability of the approach on problems with stochasticity in nonlinear dynamics, obstacle fields, interactions, and terrain parameters.

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Thomas Lew, Riccardo Bonalli, Marco Pavone. 2023-07-06. Risk-Averse Trajectory Optimization via Sample Average Approximation. https://arxiv.org/abs/2307.03167

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