  'Sharing of statistical strength' is a phrase often employed in machine
learning and signal processing. In sensor networks, for example, missing
signals from certain sensors may be predicted by exploiting their correlation
with observed signals acquired from other sensors. For humans, our hands move
synchronously with our legs, and we can exploit these implicit correlations for
predicting new poses and for generating new natural-looking walking sequences.
We can also go much further and exploit this form of transfer learning, to
develop new control schemas for robust control of rehabilitation robots. In
this short paper we introduce coregionalised locomotion envelopes - a method
for multi-dimensional manifold regression, on human locomotion variates. Herein
we render a qualitative description of this method.
