人形机器人操控接口：无需机器人演示的全身体验操控
Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations
AI总结：
HuMI通过便携硬件实现无需机器人演示的全身体验操控，提升数据收集效率并提高未见环境中的任务成功率。
摘要：
当前的人形全身体验操控方法主要依赖遥控或视觉仿真到现实的强化学习，受到硬件物流和复杂奖励工程的限制。因此，展示的自主技能仍然有限，通常局限于受控环境。在本文中，我们提出了人形操控接口（HuMI），一种便携且高效的框架，用于在各种环境中学习多种全身体验任务。HuMI通过使用便携硬件捕获丰富的全身体验数据，实现无需机器人的数据收集。这些数据驱动了一个分层学习流程，将人类动作转化为灵活且可行的人形技能。在五个全身体验任务——包括跪下、蹲下、投掷、行走和双臂操作——的广泛实验中，证明HuMI相比遥控实现了数据收集效率提升3倍，并在未见环境中达到了70%的成功率。
英文摘要：
Current approaches for humanoid whole-body manipulation, primarily relying on teleoperation or visual sim-to-real reinforcement learning, are hindered by hardware logistics and complex reward engineering. Consequently, demonstrated autonomous skills remain limited and are typically restricted to controlled environments. In this paper, we present the Humanoid Manipulation Interface (HuMI), a portable and efficient framework for learning diverse whole-body manipulation tasks across various environments. HuMI enables robot-free data collection by capturing rich whole-body motion using portable hardware. This data drives a hierarchical learning pipeline that translates human motions into dexterous and feasible humanoid skills. Extensive experiments across five whole-body tasks--including kneeling, squatting, tossing, walking, and bimanual manipulation--demonstrate that HuMI achieves a 3x increase in data collection efficiency compared to teleoperation and attains a 70% success rate in unseen environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
