Learning walk and trot from the same objective using different types of exploration

In nature, animals have developed extensive gaits to adapt to the different terrestrial terrain and situations, such as a horse galloping for faster speed, or a lizard trotting for a stable locomotion. In recent years, quadrupedal gait learning has attracted some research interest in robotics. Quadruped gaits offer a wide range of different movement patterns. As the cyclic movements of all four legs are similar, the gaits can be categorized mainly by the timing and order of the footfall, which can be represented as phase gaps among the trajectories of each leg. In the presented work we learn open loop control policies for various gaits, focusing on walk and trot. In walk the leg trajectories are separated by quarter-phase gaps, resulting in an equidistant footfall, whereas in trot diagonal pairs of legs move synchronously and are separated by half-phase gaps. Other gaits that can be learned using the described approach are bound and pace. We show how these symmetry properties can be encoded in the parameter space of the chosen policy representation, in order to enhance the initial exploration and reliably learn the chosen gaits. Neither do we fully define the gait in the policy representation as in [7, 8, 12, 3], nor do we learn random gaits [6] which could lead to a highly non convex problem.