论文标题
学习具有时间变异推理的机器人技能
Learning Robot Skills with Temporal Variational Inference
论文作者
论文摘要
在本文中，我们以无监督的方式从演示中发现了机器人选项的发现。具体而言，我们提出了一个框架，以共同学习如何通过执行各种任务的机器人演示来使用它们的低级控制策略和高级政策。通过将选项表示为连续的潜在变量，我们将学习这些选项作为潜在变量推理的问题构成了问题。然后，我们基于轨迹可能性的时间分解来提出变异推理的时间表述，这使我们能够以无监督的方式推断选项。我们演示了框架在三个机器人演示数据集中学习此类选项的能力。
In this paper, we address the discovery of robotic options from demonstrations in an unsupervised manner. Specifically, we present a framework to jointly learn low-level control policies and higher-level policies of how to use them from demonstrations of a robot performing various tasks. By representing options as continuous latent variables, we frame the problem of learning these options as latent variable inference. We then present a temporal formulation of variational inference based on a temporal factorization of trajectory likelihoods,that allows us to infer options in an unsupervised manner. We demonstrate the ability of our framework to learn such options across three robotic demonstration datasets.
