Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains. However, while gaits can be varied typically by selecting from a range of pre-computed styles, current planners are unable to vary key gait parameters continuously while the robot is in motion. The synthesis, on-the-fly, of gaits with unexpected operational characteristics or even the blending of dynamic manoeuvres lies beyond the capabilities of the current state-of-the-art. In this work we address this limitation by learning a latent space capturing the key stance phases of a particular gait, via a generative model trained on a single trot style. This 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. In fact properties of this drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. 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 a real ANYmal quadruped robot and demonstrate that our method achieves a continuous blend of dynamic trot styles whilst being robust and reactive to external perturbations.
翻译:四振的动作正在迅速成熟到一个高度,机器人现在经常穿越各种不结构的地形。然而,虽然曲调通常可以通过从一系列预选的风格中选择,而从典型的组合中选择,而目前的规划者无法在机器人运动时连续地改变关键步数参数。在飞行时,将带有出乎意料的操作特点的曲目合成成形,甚至将动态动作混合在一起,超出了当前状态的状态能力。在这项工作中,我们通过学习一个隐蔽的空间来捕捉某一特定动作的关键姿势阶段,通过以单一阵列风格训练的基因模型来应对这一局限性。这鼓励了将驱动信号应用到潜在状态的单一维度,从而引发了整体计划,合成了连续的形形形形形形形色。事实上,这种驱动信号图的特性超出了当前状态、脚步势高度和全姿势状态的参数。使用基因化模型有助于探测和减缓扰动,以提供一个灵活和健全的规划框架。我们评估了如何将驱动式信号应用到一个动态的外部模式,从而实现一个动态的机器人式样状式和连续的机器人。