  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.
