RedirectMimic: Local Motion Content Redaction for Humanoid Whole-Body Control
Overview
This paper presents RedirectMimic, a technique that enables local motion content redaction for humanoid whole-body control by separating the reference observed by the actor from the target used to train its execution. The method uses a rule-generated edited reference to guide tracking rewards during proximal policy optimization while keeping deployed observation and action interfaces unchanged. Evaluated on Unitree G1 simulation with a tracker pretrained on 7,509 motion clips, RedirectMimic reduces core-window content occurrence to 2.7% on the overhead-arm task (compared to 58.9% for reward zeroing and 32.8% for anti-tracking), achieves 94.2% core-window joint success, combines content suppression with fall-free execution and local retention, and maintains low replacement error while tracking unedited motions with accuracy comparable to the pretrained controller.
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Environment: simulation
“rectMimic, which separates the reference observed by the actor from the target used to train its execution. A rule-generated edited reference guides tracking rewards during proximal policy optimization, while the deployed observation and action interfaces remain unchanged. We evaluate pose- and speed-defined arm editing in Unitree G1 simulation using a tracker pretrained on 7,509 motion clips. On the overhead-arm tas”
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Reported quantity · motion clips count: 7509 other · Basis not reported
“rectMimic, which separates the reference observed by the actor from the target used to train its execution. A rule-generated edited reference guides tracking rewards during proximal policy optimization, while the deployed observation and action interfaces remain unchanged. We evaluate pose- and speed-defined arm editing in Unitree G1 simulation using a tracker pretrained on 7,509 motion clips. On the overhead-arm tas”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRedirectMimic: Local Motion Content Redaction for Humanoid Whole-Body Control
This paper presents RedirectMimic, a technique that enables local motion content redaction for humanoid whole-body control by separating the reference observed by the actor from the target used to train its execution. The method uses a rule-generated edited reference to guide tracking rewards during proximal policy optimization while keeping deployed observation and action interfaces unchanged. Evaluated on Unitree G1 simulation with a tracker pretrained on 7,509 motion clips, RedirectMimic reduces core-window content occurrence to 2.7% on the overhead-arm task (compared to 58.9% for reward zeroing and 32.8% for anti-tracking), achieves 94.2% core-window joint success, combines content suppression with fall-free execution and local retention, and maintains low replacement error while tracking unedited motions with accuracy comparable to the pretrained controller.
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