用单步反向神经网络加速机器人操作动作生成。
Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation
- 基于可逆神经网络的单步去噪生成动作，避免迭代计算。
- 实测推理延迟从110毫秒降至61毫秒，效率提升近50%。
- 适合需快速响应的视觉-语言-动作机器人系统部署。
本文提出一种用于通用机器人操作的可逆神经网络适配器，通过单步去噪过程，根据多模态观测（视觉、语言、本体感知）生成高维精确动作。基于流匹配框架，该适配器将动作生成轨迹约束在可逆潜在空间中，实现仅需一次推理即可高效生成高质量灵巧动作。相比传统迭代式流匹配策略，新方法显著降低推理复杂度，同时保持高精度与稳定性。在多种仿真基准和真实机器人平台上进行的大量实验表明：在仿真任务中表现优于或接近当前最优水平；在真实场景中，视觉-语言-动作模型平均推理延迟由110毫秒降至61毫秒，任务性能保持优异。
原文摘要 · Abstract (English)
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.
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