Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators and represented by learned classifiers operating upon the latent space. As a consequence, complex constraints are rendered differentiable and evaluated an order of magnitude faster than analytical approaches. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-world ANYmal quadruped. Our results demonstrate that this approach is capable of generating smooth and realisable trajectories. To the best of our knowledge, this is the first time latent space control has been successfully applied to a complex, real robot platform.

翻译：传统的四重控制方法经常采用简化的手造模型。这大大降低了机器人的能力,因为其有效的运动范围受到限制。此外,运动力限制往往没有差别,而且很难在优化方法中加以执行。在这项工作中,通过将四重控制设定为结构化潜层空间的优化来应对这些挑战。深层基因化模型捕捉了可行的联合配置的统计代表性,而复杂的动态和终端限制则通过高层次的语义指标来表达,并代表着在潜伏空间运行的有知识的分类师。因此,复杂的限制变得不同,其规模评估速度比分析方法快。我们验证了在模拟和现实世界中采用优化的移动轨迹的可行性。我们的结果表明,这种方法能够产生光滑和真实的轨迹。据我们所知,这是首次成功地将潜伏空间控制应用到一个复杂、真实的机器人平台上。