Humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control in the setting of autonomous robotic operations. We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control. Here, temporal depth refers to the nested time scales at which successive levels of a forward or generative model unfold, for example, delivering an object requires a global plan to contextualise the fast coordination of multiple local movements of limbs. This separation of temporal scales also motivates robotics and control. Specifically, to achieve versatile sensorimotor control, it is advantageous to hierarchically structure the planning and low-level motor control of individual limbs. We use numerical and physical simulation to conduct experiments and to establish the efficacy of this formulation. Using a hierarchical generative model, we show how a humanoid robot can autonomously complete a complex task that necessitates a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities. Our findings demonstrated the effectiveness of using human-inspired motor control algorithms, and our method provides a viable hierarchical architecture for the autonomous completion of challenging goal-directed tasks.

翻译：人类在与环境交互时，通过规划、执行和组合个体肢体运动，能够产生复杂的全身运动。我们在自主机器人操作环境中研究了这一运动控制的基本方面。我们采用配备多级规划的层级生成式建模方法——以实现自主任务完成——模拟了人类运动控制的深层时间架构。此处的时间深度指生成模型或前向模型连续层级所涉及的嵌套时间尺度，例如传递物品需要全局计划来协调肢体多个局部运动的快速协同。这种时间尺度的分离同样启发了机器人学与控制领域。具体而言，为实现多变的感知运动控制，将个体肢体的规划与低级运动控制进行层级结构化是有利的。我们利用数值模拟与物理仿真开展实验，验证了这一公式的有效性。通过层级生成式模型，我们展示了人形机器人如何自主完成需要整体运用移动、操纵与抓取能力的复杂任务。具体而言，我们证明了人形机器人能够自主搬运箱子、开门并穿越门廊抵达目的地、接近并踢足球，同时在身体损伤及地面不平整情况下仍保持稳健性能。研究结果表明，采用类人运动控制算法具有有效性，我们的方法为自主完成高难度目标导向任务提供了一种可行的层级架构。