将静态训练的视觉运动策略直接用于动态机器人操作，实现厘米级精度。
STDArm: Transferring Visuomotor Policies From Static Data Training to Dynamic Robot Manipulation
- 通过实时动作校正框架提升控制频率并保持时序一致。
- 在四种移动平台上的实验验证了厘米级操作精度。
- 适合希望快速部署视觉运动策略的机器人研发人员。
近期四足机器人和无人机等移动机器人平台的发展，推动了在日益动态环境中部署视觉运动策略的需求。然而，高质量训练数据的获取、平台运动的影响与处理延迟，以及有限的机载计算资源，构成了现有方案的主要障碍。本文提出STDArm系统，可直接将静态条件下训练的策略迁移到动态平台，无需大量修改。其核心为一个实时动作校正框架，包括：(1) 动作管理器以提升控制频率并维持时序一致性；(2) 基于轻量预测网络的稳定器，用于补偿运动干扰；(3) 在线延迟估计模块，用于校准系统参数。该方法在两种机械臂、四种移动平台及三项任务上进行了全面评估。实验表明，STDArm能实现实时补偿平台运动干扰，同时保留原策略的操作能力，在机器人运动过程中达到厘米级操作精度。
原文摘要 · Abstract (English)
Recent advances in mobile robotic platforms like quadruped robots and drones have spurred a demand for deploying visuomotor policies in increasingly dynamic environments. However, the collection of high-quality training data, the impact of platform motion and processing delays, and limited onboard computing resources pose significant barriers to existing solutions. In this work, we present STDArm, a system that directly transfers policies trained under static conditions to dynamic platforms without extensive modifications. The core of STDArm is a real-time action correction framework consisting of: (1) an action manager to boost control frequency and maintain temporal consistency, (2) a stabilizer with a lightweight prediction network to compensate for motion disturbances, and (3) an online latency estimation module for calibrating system parameters. In this way, STDArm achieves centimeter-level precision in mobile manipulation tasks. We conduct comprehensive evaluations of the proposed STDArm on two types of robotic arms, four types of mobile platforms, and three tasks. Experimental results indicate that the STDArm enables real-time compensation for platform motion disturbances while preserving the original policy's manipulation capabilities, achieving centimeter-level operational precision during robot motion.
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