Quadruped locomotion is rapidly maturing to a degree where robots are able to realise highly dynamic manoeuvres. However, current planners are unable to vary key gait parameters of the in-swing feet midair. In this work we address this limitation and show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. This is achieved via a generative model trained on a single trot style, which encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesising a continuous variety of trot styles. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. Due to the nature of our approach these synthesised gaits are continuously variable online during robot operation. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework. We evaluate our approach on two versions of the real ANYmal quadruped robots and demonstrate that our method achieves a continuous blend of dynamic trot styles whilst being robust and reactive to external perturbations.

翻译：四足运动技术正迅速成熟，使机器人能够实现高度动态的机动动作。然而，当前规划器无法调节摆动腿在空中的关键步态参数。本研究针对这一局限，通过学习捕捉特定步态中关键支撑阶段的潜空间，证明这对提升控制器鲁棒性至关重要。该方法基于单一小跑步态训练的生成模型，通过促进解耦使得对潜状态单一维度的驱动信号应用，能够生成涵盖连续多样化小跑步态的整体规划。我们证实驱动信号的特定属性直接映射至步频、步高及完全支撑时长等步态参数。由于方法特性，这些合成步态可在机器人运行过程中在线连续变化。利用生成模型可检测并抑制干扰，从而构建通用且鲁棒的规划框架。我们在两款真实ANYmal四足机器人上评估了该方法，证明其能在保持对外部扰动鲁棒性与响应性的同时，实现动态小跑步态的连续融合。