Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation
For robotic manipulation, this work addresses the computational bottleneck of iterative flow-matching policies, enabling faster and more efficient action generation for real-time applications.
This paper introduces an invertible neural network adapter for one-step flow matching in robot manipulation, achieving a 45% reduction in inference latency (from 110 ms to 61 ms) while maintaining strong task performance across simulation and real-world benchmarks.
This paper presents an invertible neural network adapter for general robotic manipulation, designed to generate precise high-dimensional actions conditioned on multimodal observations, including visual, linguistic, and proprioceptive inputs, through a one-step denoising process. Built upon a flow-matching formulation, the proposed adapter effectively constrains the action generation trajectory within an invertible latent space, thereby enabling efficient and high-quality dexterous action synthesis with only a single inference step. Compared with conventional iterative flow-matching policies, the proposed framework substantially reduces inference complexity while maintaining strong action prediction accuracy and stability. Extensive experiments are conducted across a diverse set of simulation benchmarks and real-world robotic platforms to evaluate the effectiveness of the proposed method. Across simulation benchmarks, the proposed adapter consistently demonstrates superior or near state-of-the-art performance on a wide range of manipulation tasks. Furthermore, real-world experiments reveal a significant improvement in inference efficiency for vision-language-action (VLA) models, reducing the average inference latency from 110 ms to 61 ms while maintaining strong task performance.