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 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 and robustly capture a richness of movement significantly exceeding the relatively narrow behaviour seen during training. In addition, 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:被四分五裂的摇摆正在迅速成熟到一个高度,机器人现在经常在各种不结构的地形中穿行。然而,尽管曲调通常可以通过从一系列预编的风格中选择,而使曲调变异,但当机器人在运动时,目前的规划者无法连续地改变关键曲调参数。在飞行中,以出乎意料的操作特点合成曲调,甚至混合动态动作,超出了当前状态的功能。在这项工作中,我们通过学习潜伏空间捕捉构成特定动作的关键姿势阶段来应对这一局限性。这是通过一个以单一阵列风格训练的外型变异模型实现的,这种变异模式鼓励将驱动信号应用到潜在状态的单一层面,从而导致整体计划合成连续的曲调风格。我们展示了驱动信号图的具体特性,直达音调参数,例如音调、步调高度和全姿势持续时间。由于我们的方法的性质,这些合成的曲调在机器人操作期间持续地进行在线变换,并强有力地捕捉到一个动态变现的机型模型,从而大大地展示了我们稳的机变的机变的机型模型。