  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.

Robotics;Artificial Intelligence