  In recent years, a number of tools have become available that recover the
underlying control policy from constrained movements. However, few have
explicitly considered learning the constraints of the motion and ways to cope
with unknown environment. In this paper, we consider learning the null space
projection matrix of a kinematically constrained system in the absence of any
prior knowledge either on the underlying policy, the geometry, or
dimensionality of the constraints. Our evaluations have demonstrated the
effectiveness of the proposed approach on problems of differing dimensionality,
and with different degrees of non-linearity.
