  With the research into development of quadruped robots picking up pace,
learning based techniques are being explored for developing locomotion
controllers for such robots. A key problem is to generate leg trajectories for
continuously varying target linear and angular velocities, in a stable manner.
In this paper, we propose a two pronged approach to address this problem.
First, multiple simpler policies are trained to generate trajectories for a
discrete set of target velocities and turning radius. These policies are then
augmented using a higher level neural network for handling the transition
between the learned trajectories. Specifically, we develop a neural
network-based filter that takes in target velocity, radius and transforms them
into new commands that enable smooth transitions to the new trajectory. This
transformation is achieved by learning from expert demonstrations. An
application of this is the transformation of a novice user's input into an
expert user's input, thereby ensuring stable manoeuvres regardless of the
user's experience. Training our proposed architecture requires much less expert
demonstrations compared to standard neural network architectures. Finally, we
demonstrate experimentally these results in the in-house quadruped Stoch 2.
