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 A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates.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.

翻译：本文提出了一种在线意图预测框架，用于实时估计自主系统的目标状态，即使在意图随时间变化、系统动力学或目标函数包含未知参数的情况下也能适用。该问题被建模为逆最优控制/逆向强化学习任务，其中意图被视作目标函数中的一个参数。滑动时域策略能够衰减过时信息，而在线控制信息学习则实现了高效的梯度计算与参数在线更新。在不同噪声水平下的仿真实验以及四旋翼无人机硬件实验表明，所提方法能够在复杂环境中实现准确且自适应的意图预测。