Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance of MOB deteriorates due to the model uncertainty which encompasses the modeling errors and the joint friction. Moreover, the estimation error is significant when MOB is applied to high-dimensional floating-base humanoids, which prevents the estimated external joint torque from being used for force control or collision detection in the real humanoid robot. In this paper, the pure external joint torque estimation method named MOB-Net, is proposed for humanoids. MOB-Net learns the model uncertainty torque and calibrates the estimated signal of MOB. The external joint torque can be estimated in the generalized coordinate including whole-body and virtual joints of the floating-base robot with only internal sensors (an IMU on the pelvis and encoders in the joints). Our method substantially reduces the estimation errors of MOB, and the robust performance of MOB-Net for the unseen data is validated through extensive simulations, real robot experiments, and ablation studies. Finally, various collision handling scenarios are presented using the estimated external joint torque from MOB-Net: contact wrench feedback control for locomotion, collision detection, and collision reaction for safety.

翻译：动量观测器（MOB）可在无需力/力矩传感器或关节力矩传感器等额外传感器的情况下估计外部关节力矩。然而，由于模型不确定性（包括建模误差和关节摩擦），MOB的估计性能会退化。此外，当MOB应用于高维浮动基座仿人机器人时，估计误差显著增大，这阻碍了将估计的外部关节力矩用于真实仿人机器人的力控制或碰撞检测。本文针对仿人机器人提出了名为MOB-Net的纯外部关节力矩估计方法。MOB-Net学习模型不确定性力矩并对MOB的估计信号进行校准。仅利用内部传感器（骨盆处惯性测量单元及关节编码器），即可在广义坐标系（包括浮动基座机器人的全身关节和虚拟关节）中估计外部关节力矩。该方法显著降低了MOB的估计误差，并通过大量仿真、真实机器人实验及消融研究验证了MOB-Net对未见数据的鲁棒性能。最后，展示了使用MOB-Net估计的外部关节力矩实现的各种碰撞处理场景：用于步态控制的接触力反馈调节、碰撞检测及安全保障碰撞反应。