A Unified Inertial Encoder for Human and Humanoid Motion
Overview
This paper introduces a unified inertial encoder that leverages kinematic IMU synthesis to align human and humanoid motion representations. By jointly training on human datasets and retargeted humanoid motions, the model achieves zero-shot transfer to a physical Unitree G1 robot without physics simulation or fine-tuning. The approach demonstrates superior velocity estimation accuracy (0.099 m/s error) across five datasets and enables language-grounded motion understanding for motion categories not present in the original human training data.
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Data origin: robot trajectories
“nitree G1 without physics simulation, real-robot training trajectories, or fine-tuning, achieving the lowest velocity error among evaluated methods across five datasets, including 0.099m/s on real G1. Its motion-description retrieval also extends to retargeted G1 motion categories observed only in human training, demonstrating how human motion knowledge can support the generalization of humanoid inertial odometry and”
Exact source · revision 1Source owner not reported
Reported quantity · velocity error: 0.099 other · Basis not reported
“nitree G1 without physics simulation, real-robot training trajectories, or fine-tuning, achieving the lowest velocity error among evaluated methods across five datasets, including 0.099m/s on real G1. Its motion-description retrieval also extends to retargeted G1 motion categories observed only in human training, demonstrating how human motion knowledge can support the generalization of humanoid inertial odometry and”
Exact source · revision 1Source owner not reported
Reported quantity · datasets evaluated: 5 other · Basis not reported
“nitree G1 without physics simulation, real-robot training trajectories, or fine-tuning, achieving the lowest velocity error among evaluated methods across five datasets, including 0.099m/s on real G1. Its motion-description retrieval also extends to retargeted G1 motion categories observed only in human training, demonstrating how human motion knowledge can support the generalization of humanoid inertial odometry and”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalA Unified Inertial Encoder for Human and Humanoid Motion
This paper introduces a unified inertial encoder that leverages kinematic IMU synthesis to align human and humanoid motion representations. By jointly training on human datasets and retargeted humanoid motions, the model achieves zero-shot transfer to a physical Unitree G1 robot without physics simulation or fine-tuning. The approach demonstrates superior velocity estimation accuracy (0.099 m/s error) across five datasets and enables language-grounded motion understanding for motion categories not present in the original human training data.
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