Learning Null Space Projections

Many everyday human skills can be considered in terms of performing some task subject to a set of self-imposed or environmental constraints. In recent years, a number of new tools have become available in the learning and robotics community that allow data from constrained and/or redundant systems to be used to uncover underlying consistent behaviours that may be otherwise masked by the constraints. However, while a wide variety of work for generalisation of movements have been proposed, few have explicitly considered learning the constraints of the motion and ways to cope with unknown environment. In this paper, we propose a method to learn the constraints such that some previously learnt behaviours can be adapted to new environment in an appropriate way. In particular, we consider learning the null space projection matrix of a kinematically constrained system, and see how previously learnt policies can be adapted to novel constraints.