FOCUS：用于稳健人形本体里程计的足部观测置信度
FOCUS: Foot Observation Confidence for Robust Humanoid Proprioceptive Odometry
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- Wuhan University（武汉大学）
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中文总结 AI 辅助
该研究提出FOCUS模型，通过预测足部正运动学的连续可靠性权重，改进人形本体里程计，在模拟与真实实验中显著降低了绝对轨迹误差，提升了里程计的稳健性。
中文摘要 AI 辅助
足部正运动学（FK）被广泛用于在足部支撑阶段提供可靠的速度约束，以改进人形机器人的本体感知腿式里程计。现有的接触辅助估计器通常依赖二值接触决策来确定是否应信任整个足部的FK测量值。然而，接触并不一定意味着FK可靠。动态运动常常涉及部分支撑、脚趾拖拽和足部打滑，导致二值接触决策在长轨迹上积累显著漂移。为解决这一局限，我们提出FOCUS（来自未标注模拟的足部观测置信度），它为每只足部预测连续的FK可靠性权重，而非估计二值足部接触状态。预测的可靠性权重不替代基于模型的估计器，而是用于将FK速度观测与IMU传播的本体速度融合，并调整扩展卡尔曼滤波器（EKF）的观测协方差，实现无硬接触切换的平滑可靠性感知融合。该网络使用自动生成的模拟信号训练，采用FK加权速度一致性损失和轻量级模拟器接触正则化，无需手动标注的连续FK可靠性标签。部署的模型仅依赖IMU和关节运动学测量，适用于扭矩传感不可靠的硬件平台。实验表明，FOCUS在模拟行走片段上将绝对轨迹误差（ATE）降低83.7%，在运动尺度和频谱能量上保留模拟动态运动保真度，在19个真实行走片段上将ATE降低70.8%，在4个真实动态运动例程上将平均ATE降低42.7%。
英文摘要
Foot forward kinematics (FK) is widely used to improve proprioceptive legged odometry by providing reliable velocity constraints during foot support. Existing contact-aided estimators generally rely on binary contact decisions to determine whether the FK measurements of an entire foot should be trusted. However, contact does not necessarily imply FK reliability. Dynamic locomotion often involves partial support, toe dragging, and foot slip, causing binary contact decisions to accumulate significant drift over long trajectories. To address this limitation, we propose FOCUS (Foot Observation Confidence from Unannotated Simulation), which predicts a continuous FK reliability weight for each foot instead of estimating binary foot contact. Rather than replacing the model-based estimator, the predicted reliability weights are used to blend FK velocity observations with IMU-propagated body velocity and to adapt the observation covariance of an extended Kalman filter (EKF), enabling smooth reliability-aware fusion without hard contact switching. The network is trained from automatically generated simulation signals using an FK-weighted velocity consistency loss with lightweight simulator-contact regularization, without manually annotated continuous FK-reliability labels. The deployed model relies only on IMU and joint kinematic measurements, making it suitable for hardware platforms with unreliable torque sensing. Experiments demonstrate that FOCUS reduces absolute trajectory error (ATE) by 83.7% on simulated walking episodes, preserves simulated dynamic-motion fidelity in motion scale and spectral energy, reduces ATE by 70.8% across 19 real walking segments, and reduces mean ATE by 42.7% across four real dynamic-motion routines.
