  Humanoid robots have complex bodies and kinematic chains with several
Degrees-of-Freedom (DoF) which are difficult to model. Learning the parameters
of a kinematic model can be achieved by observing the position of the robot
links during prospective motions and minimising the prediction errors. This
work proposes a movement efficient approach for estimating online the
body-schema of a humanoid robot arm in the form of Denavit-Hartenberg (DH)
parameters. A cost-sensitive active learning approach based on the A-Optimality
criterion is used to select optimal joint configurations. The chosen joint
configurations simultaneously minimise the error in the estimation of the body
schema and minimise the movement between samples. This reduces energy
consumption, along with mechanical fatigue and wear, while not compromising the
learning accuracy. The work was implemented in a simulation environment, using
the 7DoF arm of the iCub robot simulator. The hand pose is measured with a
single camera via markers placed in the palm and back of the robot's hand. A
non-parametric occlusion model is proposed to avoid choosing joint
configurations where the markers are not visible, thus preventing worthless
attempts. The results show cost-sensitive active learning has similar accuracy
to the standard active learning approach, while reducing in about half the
executed movement.
