Learning accurate models of the physical world is required for a lot of robotic manipulation tasks. However, during manipulation, robots are expected to interact with unknown workpieces so that building predictive models which can generalize over a number of these objects is highly desirable. In this paper, we study the problem of designing deep learning agents which can generalize their models of the physical world by building context-aware learning models. The purpose of these agents is to quickly adapt and/or generalize their notion of physics of interaction in the real world based on certain features about the interacting objects that provide different contexts to the predictive models. With this motivation, we present context-aware zero shot learning (CAZSL, pronounced as casual) models, an approach utilizing a Siamese network architecture, embedding space masking and regularization based on context variables which allows us to learn a model that can generalize to different parameters or features of the interacting objects. We test our proposed learning algorithm on the recently released Omnipush datatset that allows testing of meta-learning capabilities using low-dimensional data. Codes for CAZSL are available at https://www.merl.com/research/license/CAZSL.

中文翻译：

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CAZSL：通过上下文泛化推模型的零样本回归

许多机器人操作任务需要学习物理世界的准确模型。然而，在操作过程中，预计机器人会与未知工件进行交互，因此非常需要构建可以泛化许多这些对象的预测模型。在本文中，我们研究了设计深度学习代理的问题，该代理可以通过构建上下文感知学习模型来泛化其物理世界模型。这些代理的目的是根据交互对象的某些特征快速适应和/或概括他们在现实世界中的交互物理概念，这些特征为预测模型提供不同的上下文。出于这个动机，我们提出了上下文感知零镜头学习（CAZSL，发音为随意）模型，这是一种利用连体网络架构的方法，基于上下文变量的嵌入空间屏蔽和正则化使我们能够学习一个模型，该模型可以泛化到交互对象的不同参数或特征。我们在最近发布的 Omnipush 数据集上测试了我们提出的学习算法，该数据集允许使用低维数据测试元学习能力。CAZSL 代码可在 https://www.merl.com/research/license/CAZSL 上获得。