论文标题
第一步:带有语义限制的潜在空间控制,用于四倍的运动
First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion
论文作者
论文摘要
四足动物控制的传统方法经常采用简化的手工衍生模型。这大大降低了机器人的有效运动范围,这大大降低了机器人的能力。此外,运动动力学的约束通常是不可分割的,并且在优化方法中难以实施。在这项工作中,这些挑战是通过将四足动物控制作为结构性潜在空间中的优化来解决的。深层生成模型捕获了可行的关节配置的统计表示,而复杂的动态和终端约束是通过高级,语义指标表达的,并由在潜在空间上运行的学习分类器表示。结果,复杂的约束是可区分的,并比分析方法更快地评估了数量级。我们验证了使用我们的方法在模拟和现实世界中的四倍体中优化的运动轨迹的可行性。我们的结果表明,这种方法能够产生平稳且可实现的轨迹。据我们所知,这是第一次成功地应用于复杂的真实机器人平台。
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.