Controlling robotic manipulators with high-dimensional action spaces for dexterous tasks is a challenging problem. Inspired by human manipulation, researchers have studied generating and using postural synergies for robot hands to accomplish manipulation tasks, leveraging the lower dimensional nature of synergistic action spaces. However, many of these works require pre-collected data from an existing controller in order to derive such a subspace by means of dimensionality reduction. In this paper, we present a framework that simultaneously discovers a synergy space and a multi-task policy that operates on this low-dimensional action space to accomplish diverse manipulation tasks. We demonstrate that our end-to-end method is able to perform multiple tasks using few synergies, and outperforms sequential methods that apply dimensionality reduction to independently collected data. We also show that deriving synergies using multiple tasks can lead to a subspace that enables robots to efficiently learn new manipulation tasks and interactions with new objects.

翻译：在人类操纵的激励下,研究人员研究如何创造和使用机器人手的姿势协同效应来完成操纵任务,利用协同动作空间的较低维度特性。然而,许多这类工作都需要现有控制器预先收集数据,以便通过减少维度来获取这样一个子空间。在本文中,我们提出了一个框架,同时发现一个协同空间和多任务政策,在这个低维行动空间上运行,以完成不同的操纵任务。我们证明,我们端到端方法能够利用少数的协同作用来完成多项任务,并超越将维度降低到独立收集的数据的顺序方法。我们还表明,利用多重任务产生的协同作用可以导致一个子空间,使机器人能够有效地学习新的操纵任务和与新天体的互动。