Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations
This addresses the challenge of learning diverse whole-body manipulation tasks for humanoid robots, though it appears incremental as it builds on existing motion capture and hierarchical learning methods.
The paper tackles the problem of humanoid whole-body manipulation by introducing the Humanoid Manipulation Interface (HuMI), which enables robot-free data collection and achieves a 3x increase in data collection efficiency and a 70% success rate in unseen environments.
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.