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Quadruped animal locomotion emerges from the interactions between the spinalcentral pattern generator (CPG), sensory feedback, and supraspinal drivesignals from the brain. Computational models of CPGs have been widely used forinvestigating the spinal cord contribution to animal locomotion control incomputational neuroscience and in bio-inspired robotics. However, thecontribution of supraspinal drive to anticipatory behavior, i.e. motor behaviorthat involves planning ahead of time (e.g. of footstep placements), is not yetproperly understood. In particular, it is not clear whether the brain modulatesCPG activity and/or directly modulates muscle activity (hence bypassing theCPG) for accurate foot placements. In this paper, we investigate theinteraction of supraspinal drive and a CPG in an anticipatory locomotionscenario that involves stepping over gaps. By employing deep reinforcementlearning (DRL), we train a neural network policy that replicates thesupraspinal drive behavior. This policy can either modulate the CPG dynamics,or directly change actuation signals to bypass the CPG dynamics. Our resultsindicate that the direct supraspinal contribution to the actuation signal is akey component for a high gap crossing success rate. However, the CPG dynamicsin the spinal cord are beneficial for gait smoothness and energy efficiency.Moreover, our investigation shows that sensing the front feet distances to thegap is the most important and sufficient sensory information for learning gapcrossing. Our results support the biological hypothesis that cats and horsesmainly control the front legs for obstacle avoidance, and that hind limbsfollow an internal memory based on the front limbs' information. Our methodenables the quadruped robot to cross gaps of up to 20 cm (50% of body-length)without any explicit dynamics modeling or Model Predictive Control (MPC).

Puppeteer and Marionette: Learning Anticipatory Quadrupedal Locomotion Based on Interactions of a Central Pattern Generator and Supraspinal Drive