Efficient Trajectory Forecasting and Generation with Conditional Flow Matching
CoRR(2024)
摘要
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
142
100× speed-up compared to diffusion-based models without sacrificing
accuracy, which is crucial for real-time decision making in robotics.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要