RoboMatch让机器人在复杂环境中高效完成长时间操作任务
RoboMatch: A Unified Mobile-Manipulation Teleoperation Platform with Auto-Matching Network Architecture for Long-Horizon Tasks
- 用驾驶舱式界面同步操控移动底盘和双臂，提升操作精度
- 引入多尺度视觉与高精度惯性反馈，精细操作成功率提升20%-30%
- 自动匹配网络分解长任务，分布式推理性能提升约40%
本文提出RoboMatch，一种面向动态环境中长时间作业的统一移动操作遥操作系统，具备自动匹配网络架构。系统通过驾驶舱式控制界面实现移动基座与双机械臂的同步操作，显著提升控制精度与数据采集效率。提出本体-视觉增强扩散策略（PVE-DP），利用离散小波变换（DWT）进行多尺度视觉特征提取，并在末端集成高精度惯性测量单元（IMU）以丰富本体感知反馈，大幅改善精细操作性能。同时设计自动匹配网络（AMN），将长周期任务分解为逻辑序列，并动态分配轻量化预训练模型实现分布式推理。实验表明，该方法使数据采集效率提升超20%，在PVE-DP下任务成功率提高20%-30%，在AMN支持下长周期推理性能提升约40%，为复杂操作任务提供稳健解决方案。
原文摘要 · Abstract (English)
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
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