Online Intention Prediction via Control-Informed Learning
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 A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates.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, adapt...
| Authors | Tianyu Zhou, Zihao Liang, Zehui Lu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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