Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation

• URL: http://arxiv.org/abs/2606.19194v1
• Date: Wed, 17 Jun 2026 15:35:27 GMT
• Title: Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation
• Abstract summary: This paper presents an invertible neural network adapter for general robotic manipulation.<n>Built upon a flow-matching formulation, the proposed adapter constrains the action generation trajectory within an invertible latent space.<n>Experiments are conducted across a diverse set of simulation benchmarks and real-world robotic platforms to evaluate the effectiveness of the proposed method.
• Score: 48.77928097573051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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