TR2020-140

CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context


    •  Zhang, W., Seto, S., Jha, D., "CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2020.
      BibTeX TR2020-140 PDF Software
      • @inproceedings{Zhang2020nov,
      • author = {Zhang, Wenyu and Seto, Skyler and Jha, Devesh},
      • title = {CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-140}
      • }
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  • Research Area:

    Robotics

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 contextaware 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.

 

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