论文标题
结构化策略表示：在任意条件的动态系统中施加稳定性
Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems
论文作者
论文摘要
我们提出了一个新的基于神经网络的动态系统的新家族。提出的动力学在全球范围内稳定，可以以任意上下文状态为条件。我们展示了这些动力学如何用作结构化机器人策略。全球稳定性是最重要，最直接的归纳偏见之一，因为它使我们能够在演示区域之外施加合理的行为。
We present a new family of deep neural network-based dynamic systems. The presented dynamics are globally stable and can be conditioned with an arbitrary context state. We show how these dynamics can be used as structured robot policies. Global stability is one of the most important and straightforward inductive biases as it allows us to impose reasonable behaviors outside the region of the demonstrations.
