HuMI: Humanoid Whole-Body Manipulation from Humanoid-Free Demonstrations
Overview
This paper introduces HuMI, a framework for learning whole-body manipulation tasks using data collected without humanoids. The method captures rich motion data via portable hardware to train hierarchical models that translate human motions into humanoid skills. Experiments on five tasks show improved data collection efficiency and performance in unseen environments.
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Event evidence and corrections
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Reported quantity · Reported success rate: 70 percent · reported trials
“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.”
Exact source · revision 1Source owner not reported
Reported quantity · data collection efficiency increase: 3 other · Basis not reported
“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.”
Exact source · revision 1Source owner not reported
Reported quantity · number of environments tested: 5 other · Basis not reported
“ation Interface (HuMI), a portable and efficient framework for learning diverse whole-body manipulation tasks across various environments. HuMI enables humanoid-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--”
Exact source · revision 1Source owner not reported
Reported quantity · number of tasks: 5 other · Basis not reported
“ation Interface (HuMI), a portable and efficient framework for learning diverse whole-body manipulation tasks across various environments. HuMI enables humanoid-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--”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalHuMI: Humanoid Whole-Body Manipulation from Humanoid-Free Demonstrations
This paper introduces HuMI, a framework for learning whole-body manipulation tasks using data collected without humanoids. The method captures rich motion data via portable hardware to train hierarchical models that translate human motions into humanoid skills. Experiments on five tasks show improved data collection efficiency and performance in unseen environments.
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