For a robot to be both autonomous and collaborative requires the ability to adapt its movement to a variety of external stimuli, whether these come from humans or other robots. Typically, legged robots have oscillation periods explicitly defined as a control parameter, limiting the adaptability of walking gaits. Here we demonstrate a virtual quadruped robot employing a bio-inspired central pattern generator (CPG) that can spontaneously synchronize its movement to a range of rhythmic stimuli. Multi-objective evolutionary algorithms were used to optimize the variation of movement speed and direction as a function of the brain stem drive and the center of mass control respectively. This was followed by optimization of an additional layer of neurons that filters fluctuating inputs. As a result, a range of CPGs were able to adjust their gait pattern and/or frequency to match the input period. We show how this can be used to facilitate coordinated movement despite differences in morphology, as well as to learn new movement patterns.

翻译：为使机器人既自主又协作，需要其能够适应来自人类或其他机器人的各种外部刺激并调整运动。通常，腿式机器人的振荡周期被明确定义为控制参数，这限制了步态的自适应能力。本文展示了一个虚拟四足机器人，其采用仿生中枢模式发生器（CPG），可自发地将其运动同步到一系列节奏性刺激中。我们使用多目标进化算法分别优化运动速度和方向随脑干驱动及质心控制的变化。随后，通过优化一个额外的神经元层来过滤波动输入。结果显示，一系列CPG能够调整其步态模式和/或频率以匹配输入周期。我们展示了这如何促进形态差异下的协调运动，以及如何学习新的运动模式。