Delta flow method improves efficiency and smoothness in robot action models
$Δ$ Flow Matching for Robot Foundation Models
Robotics
Summary
Robots often learn to predict their next actions by guessing from scratch, which can be slow and jerky. The authors introduce a new way called delta flow that starts from recent history with some noise, making the prediction closer and smoother. This method allows robots to learn faster and act more smoothly with fewer steps. It also works well on existing models and helps robots handle new tasks better.
What this means in practice
- •For robotics engineers: Train robot control models more efficiently with faster convergence and smoother predicted actions using delta flow.
- •For automation system developers: Improve robot task success and generalization on manipulation tasks by applying delta flow to existing vision-action models without retraining.
Authors
Kuangji Zuo, Tuo An, Xinying Guo, Gen Li, Mengfei Zhao, Jiaqi Bai, Bofan Lyu, Zhuoyuan Yu, Jinghan Yang, Jiayi Li, Jindou Jia, Jianfei Yang
Abstract
Flow matching is a common generative recipe for robot foundation models. Vision-language-action models (VLAs) generate action chunks, while world-action models (WAMs) additionally generate future visual states. Both start generation from a fixed Gaussian prior, despite temporal continuity between past and future states and actions. This requires costly training and iterative sampling, especially for WAMs with an additional visual branch. We introduce $Δ$-flow, which constructs the source from recent history with mild noise rather than from a fixed Gaussian prior. This moves the source closer to the target, reducing transport to a small residual $Δ$ that can be learned efficiently and enables single-step inference. We integrate $Δ$-flow into the VLA policy $π_{0.5}$ and the WAM policies ImageWAM and Fast-WAM. On LIBERO, ImageWAM reaches 90% success in one epoch rather than six, and Fast-WAM reaches 95% in two epochs rather than eight. With LoRA fine-tuning, $π_{0.5}$ exceeds its baseline's five-epoch performance after one epoch. Single-step inference gives $1.54\times$, $2.49\times$, and $4.45\times$ speedups on $π_{0.5}$, ImageWAM, and Fast-WAM, respectively. It preserves action smoothness, achieving a $4.5\times$ reduction in action jerk compared with one-step Gaussian sampling. $Δ$-flow reaches state-of-the-art generalization performance on LIBERO-Plus. Action-history sources can be applied to pretrained WAM policies without retraining. Replacing the Gaussian action source at inference largely preserves task success with a single sampling step and can slightly improve it. We further study how action representation and visual information in action learning affect history-to-future prediction in $Δ$-flow. These results show that $Δ$-flow is simple yet effective, improving the efficiency and generalization of robot foundation models.