Online Intention Prediction via Control-Informed Learning

• URL: http://arxiv.org/abs/2604.09303v1
• Date: Fri, 10 Apr 2026 13:11:32 GMT
• Title: Online Intention Prediction via Control-Informed Learning
• Abstract summary: This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time.<n>A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates.
• Score: 4.548770944690356
• License: http://creativecommons.org/licenses/by/4.0/
• 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 A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates.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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