  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.
