  Recent advancements in legged locomotion research have made legged robots a
preferred choice for navigating challenging terrains when compared to their
wheeled counterparts. This paper presents a novel locomotion policy, trained
using Deep Reinforcement Learning, for a quadrupedal robot equipped with an
additional prismatic joint between the knee and foot of each leg. The training
is performed in NVIDIA Isaac Gym simulation environment. Our study investigates
the impact of these joints on maintaining the quadruped's desired height and
following commanded velocities while traversing challenging terrains. We
provide comparison results, based on a Cost of Transport (CoT) metric, between
quadrupeds with and without prismatic joints. The learned policy is evaluated
on a set of challenging terrains using the CoT metric in simulation. Our
results demonstrate that the added degrees of actuation offer the locomotion
policy more flexibility to use the extra joints to traverse terrains that would
be deemed infeasible or prohibitively expensive for the conventional
quadrupedal design, resulting in significantly improved efficiency.
