Risk-Sensitive Model Predictive Control for Interaction-Aware Planning -- A Sequential Convexification Algorithm

Fuente: arXiv
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Main Authors: Wang, Renzi, Schuurmans, Mathijs, Patrinos, Panagiotis
Format: Preprint
Published: 2025
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author Wang, Renzi
Schuurmans, Mathijs
Patrinos, Panagiotis
author_facet Wang, Renzi
Schuurmans, Mathijs
Patrinos, Panagiotis
contents This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing us to efficiently solve the generally nonconvex optimal control problem as a sequence of convex problems. Simulations of a robot navigating a corridor demonstrate the effectiveness and the computational advantage of the proposed approach.
format Preprint
id arxiv_https___arxiv_org_abs_2503_14328
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Risk-Sensitive Model Predictive Control for Interaction-Aware Planning -- A Sequential Convexification Algorithm
Wang, Renzi
Schuurmans, Mathijs
Patrinos, Panagiotis
Optimization and Control
Systems and Control
This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing us to efficiently solve the generally nonconvex optimal control problem as a sequence of convex problems. Simulations of a robot navigating a corridor demonstrate the effectiveness and the computational advantage of the proposed approach.
title Risk-Sensitive Model Predictive Control for Interaction-Aware Planning -- A Sequential Convexification Algorithm
topic Optimization and Control
Systems and Control
url https://arxiv.org/abs/2503.14328