Quadruped animal locomotion emerges from the interactions between the spinal central pattern generator (CPG), sensory feedback, and supraspinal drive signals from the brain. Computational models of CPGs have been widely used for investigating the spinal cord contribution to animal locomotion control in computational neuroscience and in bio-inspired robotics. However, the contribution of supraspinal drive to anticipatory behavior, i.e. motor behavior that involves planning ahead of time (e.g. of footstep placements), is not yet properly understood. In particular, it is not clear whether the brain modulates CPG activity and/or directly modulates muscle activity (hence bypassing the CPG) for accurate foot placements. In this paper, we investigate the interaction of supraspinal drive and a CPG in an anticipatory locomotion scenario that involves stepping over gaps. By employing deep reinforcement learning (DRL), we train a neural network policy that replicates the supraspinal drive behavior. This policy can either modulate the CPG dynamics, or directly change actuation signals to bypass the CPG dynamics. Our results indicate that the direct supraspinal contribution to the actuation signal is a key component for a high gap crossing success rate. However, the CPG dynamics in the spinal cord are beneficial for gait smoothness and energy efficiency. Moreover, our investigation shows that sensing the front feet distances to the gap is the most important and sufficient sensory information for learning gap crossing. Our results support the biological hypothesis that cats and horses mainly control the front legs for obstacle avoidance, and that hind limbs follow an internal memory based on the front limbs' information. Our method enables 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).

翻译：四足动物的运动源于脊髓中枢模式发生器（CPG）、感觉反馈以及来自大脑的脊髓上驱动信号之间的交互作用。在计算神经科学和仿生机器人学中，CPG的计算模型已被广泛用于研究脊髓对动物运动控制的贡献。然而，脊髓上驱动对预期行为（即涉及提前规划的运动行为，如立足点放置）的贡献尚未得到充分理解。特别是，大脑是否通过调节CPG活动和/或直接调节肌肉活动（从而绕过CPG）来实现精确的立足点放置，这一点尚不明确。本文研究了在涉及跨越间隙的预期运动场景中，脊髓上驱动与CPG的交互作用。通过采用深度强化学习（DRL），我们训练了一个模拟脊髓上驱动行为的神经网络策略。该策略可以调节CPG动态，或直接改变驱动信号以绕过CPG动态。结果表明，直接对驱动信号施加脊髓上贡献是提高间隙跨越成功率的关键因素。然而，脊髓中的CPG动态有利于步态平滑性和能量效率。此外，我们的研究表明，感知前足与间隙的距离是学习跨越间隙最重要且充分的感知信息。我们的结果支持了生物学假说，即猫和马主要通过控制前肢来避开障碍物，而后肢则基于前肢信息遵循内部记忆。我们的方法使四足机器人能够跨越长达20厘米（体长的50%）的间隙，而无需任何显式的动力学建模或模型预测控制（MPC）。