Trajectory prediction and generation are crucial for autonomous robots in 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. While diffusion models excel in trajectory generation, their iterative sampling process is computationally intensive, hindering robotic systems' dynamic capabilities. We introduce Trajectory Conditional Flow Matching (T-CFM), a novel approach using flow matching techniques to learn a solver time-varying vector field for efficient, fast trajectory generation. T-CFM demonstrates effectiveness in adversarial tracking, real-world aircraft trajectory forecasting, and long-horizon planning, outperforming state-of-the-art baselines with 35% higher predictive accuracy and 142% improved planning performance. Crucially, T-CFM achieves up to 100$\times$ speed-up compared to diffusion models without sacrificing accuracy, enabling real-time decision making in robotics. Codebase: https://github.com/CORE-Robotics-Lab/TCFM

翻译：轨迹预测与生成对于动态环境中的自主机器人至关重要。尽管先前的研究通常专注于预测或生成中的单一任务，但我们的方法统一了这两项任务，提供了一个多功能框架并实现了最先进的性能。虽然扩散模型在轨迹生成方面表现出色，但其迭代采样过程计算密集，阻碍了机器人系统的动态能力。我们提出了轨迹条件流匹配（T-CFM），这是一种利用流匹配技术学习求解器时变向量场以实现高效快速轨迹生成的新方法。T-CFM在对抗性跟踪、真实世界飞机轨迹预测和长时程规划中均展现出有效性，其预测准确率比最先进的基线模型高出35%，规划性能提升142%。至关重要的是，与扩散模型相比，T-CFM在保持精度的同时实现了高达100倍的加速，使机器人能够进行实时决策。代码库：https://github.com/CORE-Robotics-Lab/TCFM