This paper presents RoboMatch, a novel unified teleoperation platform for mobile manipulation with an auto-matching network architecture, designed to tackle long-horizon tasks in dynamic environments. Our system enhances teleoperation performance, data collection efficiency, task accuracy, and operational stability. The core of RoboMatch is a cockpit-style control interface that enables synchronous operation of the mobile base and dual arms, significantly improving control precision and data collection. Moreover, we introduce the Proprioceptive-Visual Enhanced Diffusion Policy (PVE-DP), which leverages Discrete Wavelet Transform (DWT) for multi-scale visual feature extraction and integrates high-precision IMUs at the end-effector to enrich proprioceptive feedback, substantially boosting fine manipulation performance. Furthermore, we propose an Auto-Matching Network (AMN) architecture that decomposes long-horizon tasks into logical sequences and dynamically assigns lightweight pre-trained models for distributed inference. Experimental results demonstrate that our approach improves data collection efficiency by over 20%, increases task success rates by 20-30% with PVE-DP, and enhances long-horizon inference performance by approximately 40% with AMN, offering a robust solution for complex manipulation tasks. Project website: https://robomatch.github.io

翻译：本文提出了RoboMatch，一种新颖的统一移动操作遥操作平台，其核心为自动匹配网络架构，旨在解决动态环境中的长程任务挑战。我们的系统提升了遥操作性能、数据收集效率、任务精度与操作稳定性。RoboMatch的核心是座舱式控制界面，可实现移动底盘与双臂的同步操控，显著提高了控制精度与数据收集能力。此外，我们引入了本体感知-视觉增强扩散策略（PVE-DP），该策略利用离散小波变换（DWT）进行多尺度视觉特征提取，并通过在末端执行器集成高精度惯性测量单元（IMU）以丰富本体感知反馈，大幅提升了精细操作性能。更进一步，我们提出了一种自动匹配网络（AMN）架构，可将长程任务分解为逻辑序列，并动态分配轻量级预训练模型进行分布式推理。实验结果表明，我们的方法将数据收集效率提升了超过20%，使用PVE-DP使任务成功率提升了20-30%，而采用AMN使长程推理性能提升了约40%，为复杂操作任务提供了稳健的解决方案。项目网站：https://robomatch.github.io