PRIMO：基于人体运动跟踪的先验信息里程计用于人形机器人
PRIMO: Prior-Informed Odometry from Human-Motion Tracking for Humanoid Robots
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中文总结 AI 辅助
PRIMO通过人体运动跟踪生成多样化训练数据，并利用物理与对称性先验及原始上下文路径，显著提升人形机器人本体感觉里程计的仿真到现实泛化能力，大幅降低误差。
中文摘要 AI 辅助
仿真训练的人形机器人本体感觉里程计面临两个迁移挑战：由特定机器人控制策略（旨在部署）生成的训练轨迹仅覆盖有限范围的运动，而仿真到现实的差异可能使无约束预测不可靠。我们通过基于人体运动跟踪的先验信息里程计（PRIMO）同时解决这两个问题。在数据方面，我们通过让人形机器人在仿真中跟踪多样化的重定向人体运动来生成里程计监督，将监督与部署策略解耦，并拓宽训练运动分布。在模型方面，先验信息估计器利用基于物理和对称性的先验来构建速度和旋转预测，并采用粗粒度原始上下文路径以在编码特征之外保留传感器上下文，从而增强仿真到现实的泛化能力。在统一的真实机器人协议下，PRIMO在每个领域-指标比较中相对于最强评估外部基线将平均误差降低了31.6%-61.7%。在两次运动策略修订中，策略专家表现出对称交叉，而跟踪-运动训练将平均相反策略仿真误差降低了86.8%-94.6%。在真实动态运动上，相对于在两种部署策略的并集上训练，跟踪-运动训练将平均误差降低了69.2%-81.7%。在测试的运动组成中，先验信息估计器相对于其无约束对应物在仿真和真实机器人评估中均持续降低平均轨迹误差。代码可在以下 https URL 获取。
英文摘要
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.
发表机构
- Wuhan University（武汉大学）
- Agibot（智元机器人）
机构由 AI 辅助整理，请以论文原文为准。
