Abstract
This work introduces a motion retargeting approach for legged robots, which aims to create motion controllers that imitate the fine behavior of animals. Our approach, namely spatio-temporal motion retargeting (STMR), guides imitation learning procedures by transferring motion from source to target, effectively bridging the morphological disparities by ensuring the feasibility of imitation on the target system. Our STMR method comprises two components: spatial motion retargeting (SMR) and temporal motion retargeting (TMR). On the one hand, SMR tackles motion retargeting at the kinematic level by generating kinematically feasible whole-body motions from keypoint trajectories. On the other hand, TMR aims to retarget motion at the dynamic level by optimizing motion in the temporal domain. We showcase the effectiveness of our method in facilitating Imitation Learning (IL) for complex animal movements through a series of simulation and hardware experiments. In these experiments, our STMR method successfully tailored complex animal motions from various media, including video captured by a hand-held camera, to fit the morphology and physical properties of the target robots. This enabled RL policy training for precise motion tracking, while baseline methods struggled with highly dynamic motion involving flying phases. Moreover, we validated that the control policy can successfully imitate six different motions in two quadruped robots with different dimensions and physical properties in real-world settings.
Abstract (translated)
这项工作提出了一种适用于下肢机器人的运动再适方法，旨在创建具有动物精细行为的运动控制器。我们的方法，即空间时间运动再适（STMR），通过将运动从源系统传到目标系统来指导模仿学习过程，有效弥合了形态差异，确保了目标系统上的模仿可行性。我们的STMR方法包括两个组件：空间运动再适（SMR）和时间运动再适（TMR）。一方面，SMR通过从关键点轨迹中生成全局运动来解决运动再适问题。另一方面，TMR旨在通过优化在时间域中的运动来再适运动。我们在一系列模拟和硬件实验中展示了我们方法的有效性，通过这些实验和硬件实验，我们的STMR方法成功地将各种媒体中的复杂动物运动进行了调整，以适应目标机器人的形态和物理属性。这使得精确运动跟踪的RL策略训练成为可能，而基于基准方法的运动在飞行阶段中具有高度动态的情况表现不佳。此外，我们还验证了控制策略可以成功地模仿六种不同的运动，这些机器人具有不同的尺寸和物理属性，在现实场景中。
URL
https://arxiv.org/abs/2404.11557
