  In this work, we provide a simulation framework to perform systematic studies
on the effects of spinal joint compliance and actuation on bounding performance
of a 16-DOF quadruped spined robot Stoch 2. Fast quadrupedal locomotion with
active spine is an extremely hard problem, and involves a complex coordination
between the various degrees of freedom. Therefore, past attempts at addressing
this problem have not seen much success. Deep-Reinforcement Learning seems to
be a promising approach, after its recent success in a variety of robot
platforms, and the goal of this paper is to use this approach to realize the
aforementioned behaviors. With this learning framework, the robot reached a
bounding speed of 2.1 m/s with a maximum Froude number of 2. Simulation results
also show that use of active spine, indeed, increased the stride length,
improved the cost of transport, and also reduced the natural frequency to more
realistic values.
