Efficient Trajectory Forecasting and Generation with Conditional Flow Matching
<|reference_start|>Efficient Trajectory Forecasting and Generation with Conditional Flow Matching: Trajectory prediction and generation are vital for autonomous robots navigating dynamic environments. While prior research has typically focused on either prediction or generation, our approach unifies these tasks to provide a versatile framework and achieve state-of-the-art performance. Diffusion models, which are currently state-of-the-art for learned trajectory generation in long-horizon planning and offline reinforcement learning tasks, rely on a computationally intensive iterative sampling process. This slow process impedes the dynamic capabilities of robotic systems. In contrast, we introduce Trajectory Conditional Flow Matching (T-CFM), a novel data-driven approach that utilizes flow matching techniques to learn a solver time-varying vector field for efficient and fast trajectory generation. We demonstrate the effectiveness of T-CFM on three separate tasks: adversarial tracking, real-world aircraft trajectory forecasting, and long-horizon planning. Our model outperforms state-of-the-art baselines with an increase of 35% in predictive accuracy and 142% increase in planning performance. Notably, T-CFM achieves up to 100$\times$ speed-up compared to diffusion-based models without sacrificing accuracy, which is crucial for real-time decision making in robotics.<|reference_end|>
@article{ye2024efficient,
    title={Efficient Trajectory Forecasting and Generation with Conditional Flow
  Matching},
    author={Sean Ye, Matthew Gombolay},
    journal={arXiv preprint arXiv:2403.10809},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10809},
    primaryClass={cs.RO}
}