  In this paper, with a view toward fast deployment of learned locomotion gaits
in low-cost hardware, we generate a library of walking trajectories, namely,
forward trot, backward trot, side-step, and turn in our custom-built quadruped
robot, Stoch 2, using reinforcement learning. There are existing approaches
that determine optimal policies for each time step, whereas we determine an
optimal policy, in the form of end-foot trajectories, for each half walking
step i.e., swing phase and stance phase. The way-points for the foot
trajectories are obtained from a linear policy, i.e., a linear function of the
states of the robot, and cubic splines are used to interpolate between these
points. Augmented Random Search, a model-free and gradient-free learning
algorithm is used to learn the policy in simulation. This learned policy is
then deployed on hardware, yielding a trajectory in every half walking step.
Different locomotion patterns are learned in simulation by enforcing a
preconfigured phase shift between the trajectories of different legs. The
transition from one gait to another is achieved by using a low-pass filter for
the phase, and the sim-to-real transfer is improved by a linear transformation
of the states obtained through regression.
