PRIMO: Prior-Informed Odometry from Human-Motion Tracking for Humanoid Robots

2026-09-20

AI Summary

该研究通过从人类运动追踪生成的多样化数据和使用物理及对称性先验信息的估计器来解决仿真训练的人形机器人本体感受里程计在实际应用中的转移挑战。

Abstract

Simulation-trained humanoid proprioceptive odometry faces two transfer challenges: training trajectories generated by specific robot control policies intended for deployment cover only a limited range of motions, while sim-to-real mismatch can make unconstrained predictions unreliable. We address both with Prior-Informed Odometry from Human-Motion Tracking (PRIMO). On the data side, we generate odometry supervision by having the humanoid track diverse retargeted human motions in simulation, decoupling supervision from the deployment policies and broadening the training motion distribution. On the model side, a Prior-Informed estimator uses physics- and symmetry-informed priors to structure velocity and rotation prediction and a coarse raw-context pathway to preserve sensor context alongside encoded features, thereby strengthening sim-to-real generalization. Under a unified real-robot protocol, PRIMO reduces mean error by 31.6%-61.7% relative to the strongest evaluated external baseline in each domain-metric comparison. Across two locomotion-policy revisions, policy specialists exhibit symmetric crossover, whereas Tracking-Locomotion training reduces mean opposite-policy simulation error by 86.8%-94.6%. On real dynamic motion, Tracking-Locomotion training reduces mean error by 69.2%-81.7% relative to training on the union of both deployment policies. Across the tested motion compositions, the Prior-Informed estimator consistently lowers mean trajectory errors relative to its Unconstrained counterpart in both simulation and real-robot evaluation. Code is available at https://github.com/Agibot-Spatial-Intelligence/PRIMO.

Problem

• Odometry
• Humanoid Robots
• Sim-to-Real Transfer
• Motion Tracking
• Control Policies

Innovation

• Prior-Informed Odometry
• Human-Motion Tracking
• Physics-informed Priors
• Sim-to-Real Generalization
• Diverse Motion Data