  Many robots utilize commercial force/torque sensors to identify inertial
properties of unknown objects. However, such sensors can be difficult to apply
to small-sized robots due to their weight, size, and cost. In this paper, we
propose a learning-based approach for estimating the mass and center of mass
(COM) of unknown objects without using force/torque sensors at the end-effector
or on the joints. In our method, a robot arm carries an unknown object as it
moves through multiple discrete configurations. Measurements are collected when
the robot reaches each discrete configuration and stops. A neural network is
designed to estimate joint torques from encoder discrepancies. Given multiple
samples, we derive the closed-form relation between joint torques and the
object's inertial properties. Based on the derivation, the mass and COM of
object are identified by weighted least squares. In order to improve the
accuracy of inferred inertial properties, an attention model is designed to
generate weights of joints, which indicate the relative importance for each
joint. Our framework requires only encoder measurements without using any
force/torque sensors, but still maintains accurate estimation capability. The
proposed approach has been demonstrated on a 4 degree of freedom (DOF) robot
arm.
