The estimation of external joint torque and contact wrench is essential for achieving stable locomotion of humanoids and safety-oriented robots. Although the contact wrench on the foot of humanoids can be measured using a force-torque sensor (FTS), FTS increases the cost, inertia, complexity, and failure possibility of the system. This paper introduces a method for learning external joint torque solely using proprioceptive sensors (encoders and IMUs) for a floating base robot. For learning, the GRU network is used and random walking data is collected. Real robot experiments demonstrate that the network can estimate the external torque and contact wrench with significantly smaller errors compared to the model-based method, momentum observer (MOB) with friction modeling. The study also validates that the estimated contact wrench can be utilized for zero moment point (ZMP) feedback control, enabling stable walking. Moreover, even when the robot's feet and the inertia of the upper body are changed, the trained network shows consistent performance with a model-based calibration. This result demonstrates the possibility of removing FTS on the robot, which reduces the disadvantages of hardware sensors. The summary video is available at https://youtu.be/gT1D4tOiKpo.

翻译：外部关节扭矩与接触力的估计是实现仿人机器人稳定行走及安全导向机器人的关键。尽管仿人机器人足部接触力可通过力-力矩传感器（FTS）测量，但FTS会增加系统成本、惯性、复杂度及故障概率。本文提出一种仅利用本体感传感器（编码器和惯性测量单元）学习浮基机器人外部关节扭矩的方法。在学习过程中，采用门控循环单元（GRU）网络并采集随机行走数据。真实机器人实验表明，相较于基于模型的方法（含摩擦建模的动量观测器MOB），该网络能以显著更小的误差估计外部扭矩与接触力。研究还验证了估计的接触力可用于零力矩点（ZMP）反馈控制，从而支持稳定行走。此外，即便改变机器人的足部结构及上身惯性，训练后的网络仍能通过基于模型的校准保持稳定性能。该结果证明了在机器人上移除FTS的可行性，从而消除硬件传感器的弊端。总结视频见https://youtu.be/gT1D4tOiKpo。