  Many robots utilize commercial force/torque sensors to identify physical
properties of a priori unknown objects. However, such sensors can be difficult
to apply to smaller-sized robots due to their weight, size, and high-cost. In
this paper, we propose a framework for smaller-sized humanoid robots to
estimate the inertial properties of unknown objects without using force/torque
sensors. In our framework, a neural network is designed and trained to predict
joint torque outputs. The neural network's inputs are robot's joint angle,
steady-state joint error, and motor current. All of these can be easily
obtained from many existing smaller-sized robots. As the joint rotation
direction is taken into account, the neural network can be trained with a
smaller sample size, but still maintains accurate torque estimation capability.
Eventually, the inertial properties of the objects are identified using a
nonlinear optimization method. Our proposed framework has been demonstrated on
a NAO humanoid robot.
