CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context
This work addresses the challenge of generalization in robotic manipulation for unknown objects, representing an incremental improvement in context-aware learning methods.
The paper tackles the problem of building predictive models for robotic manipulation that can generalize to unknown workpieces by introducing context-aware zero-shot learning (CAZSL) models, which utilize a Siamese network architecture, embedding space masking, and regularization based on context variables, and tests the algorithm on the Omnipush dataset.
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.