# Efficient Trajectory Forecasting and Generation with Conditional Flow Matching

arXiv: https://arxiv.org/abs/2403.10809 · HTML: https://mlsanity.com/paper/2403.10809/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

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

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Building Normalizing Flows with Stochastic Interpolants](https://mlsanity.com/paper/2209.15571/) (2022)
- [Improving and generalizing flow-based generative models with minibatch optimal transport](https://mlsanity.com/paper/2302.00482/) (2023)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models](https://mlsanity.com/paper/2308.01557/) (2023)
- [Simulation-free Schr"odinger bridges via score and flow matching](https://mlsanity.com/paper/2307.03672/) (2023)
- [Efficient and Trustworthy Social Navigation Via Explicit and Implicit Robot-Human Communication](https://mlsanity.com/paper/1810.11556/) (2018)
- [Learning Models of Adversarial Agent Behavior under Partial Observability](https://mlsanity.com/paper/2306.11168/) (2023)
- [Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment](https://mlsanity.com/paper/2306.11301/) (2023)
- [Diffusion Models for Multi-target Adversarial Tracking](https://mlsanity.com/paper/2307.06244/) (2023)

## Built on by (most cited first, 12 of 12)

- [Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories](https://mlsanity.com/paper/2505.21851/) (2025)
- [Inference-Time Policy Steering through Human Interactions](https://mlsanity.com/paper/2411.16627/) (2024)
- [SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions](https://mlsanity.com/paper/2509.24243/) (2025)
- [Flow Matching Ergodic Coverage](https://mlsanity.com/paper/2504.17872/) (2025)
- [Unified Generation-Refinement Planning: Bridging Guided Flow Matching and Sampling-Based MPC for Social Navigation](https://mlsanity.com/paper/2508.01192/) (2025)
- [Adaptive Time Step Flow Matching for Autonomous Driving Motion Planning](https://mlsanity.com/paper/2602.10285/) (2026)
- [Pairwise Spatiotemporal Partial Trajectory Matching for Co-movement Analysis](https://mlsanity.com/paper/2412.02879/) (2024)
- [JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems](https://mlsanity.com/paper/2606.20686/) (2026)
- [BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling](https://mlsanity.com/paper/2606.21014/) (2026)
- [History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots](https://mlsanity.com/paper/2609.25658/) (2026)
- [Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics](https://mlsanity.com/paper/2610.03132/) (2026)
- [Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics](https://mlsanity.com/paper/2610.16327/) (2026)

## Abstract (arXiv)

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× speed-up compared to diffusion models without sacrificing accuracy, enabling real-time decision making in robotics. Codebase: https://github.com/CORE-Robotics-Lab/TCFM