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arXiv · 2604.09303

Online Intention Prediction via Control-Informed Learning

Tianyu Zhou·Zihao Liang·Zehui Lu·Shaoshuai Mou
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Abstract

This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is time-varying, and system dynamics or objectives include unknown parameters. The problem is formulated as an inverse optimal control / inverse reinforcement learning task, with the intention treated as a parameter in the objective. A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates. Simulations under varying noise levels and hardware experiments on a quadrotor drone demonstrate that the proposed approach achieves accurate, adaptive intention prediction in complex environments.

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Tianyu Zhou, Zihao Liang, Zehui Lu, Shaoshuai Mou. 2026-04-10. Online Intention Prediction via Control-Informed Learning. https://arxiv.org/abs/2604.09303

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