# Online Intention Prediction via Control-Informed Learning

arXiv: https://arxiv.org/abs/2604.09303 · HTML: https://mlsanity.com/paper/2604.09303/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Model-Based Imitation Learning for Urban Driving](https://mlsanity.com/paper/2210.07729/) (2022)
- [Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction](https://mlsanity.com/paper/2002.08945/) (2020)
- [Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework](https://mlsanity.com/paper/1912.12970/) (2019)
- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [Imitation Learning for Human Pose Prediction](https://mlsanity.com/paper/1909.03449/) (2019)
- [Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs](https://mlsanity.com/paper/1912.01118/) (2019)
- [Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving](https://mlsanity.com/paper/2006.13704/) (2020)
- [Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field](https://mlsanity.com/paper/1906.10307/) (2019)
- [Online Control-Informed Learning](https://mlsanity.com/paper/2410.03924/) (2024)
- [A Data-Driven Approach for Inverse Optimal Control](https://mlsanity.com/paper/2304.00100/) (2023)
- [Safe Online Control-Informed Learning](https://mlsanity.com/paper/2512.13868/) (2025)

## Abstract (arXiv)

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 A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates.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