The current state-of-the-art in quadruped locomotion is able to produce a variety of complex motions. These methods either rely on switching between a discrete set of skills or learn a distribution across gaits using complex black-box models. Alternatively, we present Gaitor, which learns a disentangled and 2D representation across locomotion gaits. This learnt representation forms a planning space for closed-loop control delivering continuous gait transitions and perceptive terrain traversal. Gaitor's latent space is readily interpretable and we discover that during gait transitions, novel unseen gaits emerge. The latent space is disentangled with respect to footswing heights and lengths. This means that these gait characteristics can be varied independently in the 2D latent representation. Together with a simple terrain encoding and a learnt planner operating in the latent space, Gaitor can take motion commands including desired gait type and swing characteristics all while reacting to uneven terrain. We evaluate Gaitor in both simulation and the real world on the ANYmal C platform. To the best of our knowledge, this is the first work learning a unified and interpretable latent space for multiple gaits, resulting in continuous blending between different locomotion modes on a real quadruped robot. An overview of the methods and results in this paper is found at https://youtu.be/eVFQbRyilCA.

翻译：当前四足机器人运动领域的最先进技术能够产生多种复杂运动。这些方法要么依赖于在离散技能集之间切换，要么使用复杂的黑盒模型学习跨步态的分布。作为替代方案，我们提出Gaitor，它学习跨运动步态的解耦二维表示。这种学习到的表示构成了闭环控制的规划空间，实现了连续的步态转换和感知性地形穿越。Gaitor的潜在空间易于解释，我们发现在步态转换过程中会出现未见过的全新步态。该潜在空间在足部摆动高度和长度维度上实现解耦，这意味着这些步态特征可以在二维潜在表示中独立调节。结合简单的地形编码和在潜在空间中运行的学习型规划器，Gaitor能够接收包括期望步态类型和摆动特征在内的运动指令，同时应对不平坦地形。我们在仿真环境和ANYmal C平台上对Gaitor进行了真实世界评估。据我们所知，这是首个为多种步态学习统一可解释潜在空间的工作，实现了真实四足机器人不同运动模式间的连续融合。本文方法与结果概述详见https://youtu.be/eVFQbRyilCA。