实时预测动态意图，支持未知参数下的自适应系统决策。
Online Intention Prediction via Control-Informed Learning
- 将意图建模为优化目标参数，结合在线控制信息更新
- 在不同噪声水平下保持高精度，硬件实验验证有效性
- 适合无人机等动态系统在复杂环境中的实时决策
本文提出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，即使意图随时间变化，且系统动力学或目标包含未知参数。该问题被建模为逆最优控制/逆强化学习任务，将意图视为目标函数中的参数。采用滑动窗口策略淡化过时信息，结合在线控制感知学习实现高效梯度计算与参数更新。在不同噪声水平下的仿真以及四旋翼无人机的硬件实验表明，所提方法在复杂环境中实现了准确、自适应的意图预测。
原文摘要 · Abstract (English)
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 A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates.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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