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arXiv · 1910.01994

Zero Shot Learning on Simulated Robots

Robert Kwiatkowski·Hod Lipson
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Abstract

In this work we present a method for leveraging data from one source to learn how to do multiple new tasks. Task transfer is achieved using a self-model that encapsulates the dynamics of a system and serves as an environment for reinforcement learning. To study this approach, we train a self-models on various robot morphologies, using randomly sampled actions. Using a self-model, an initial state and corresponding actions, we can predict the next state. This predictive self-model is then used by a standard reinforcement learning algorithm to accomplish tasks without ever seeing a state from the "real" environment. These trained policies allow the robots to successfully achieve their goals in the "real" environment. We demonstrate that not only is training on the self-model far more data efficient than learning even a single task, but also that it allows for learning new tasks without necessitating any additional data collection, essentially allowing zero-shot learning of new tasks.

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Robert Kwiatkowski, Hod Lipson. 2019-10-04. Zero Shot Learning on Simulated Robots. https://arxiv.org/abs/1910.01994

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