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arXiv · 1710.02714

Interactive Learning of State Representation through Natural Language Instruction and Explanation

Qiaozi Gao·Lanbo She·Joyce Y. Chai
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Abstract

One significant simplification in most previous work on robot learning is the closed-world assumption where the robot is assumed to know ahead of time a complete set of predicates describing the state of the physical world. However, robots are not likely to have a complete model of the world especially when learning a new task. To address this problem, this extended abstract gives a brief introduction to our on-going work that aims to enable the robot to acquire new state representations through language communication with humans.

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Qiaozi Gao, Lanbo She, Joyce Y. Chai. 2017-10-07. Interactive Learning of State Representation through Natural Language Instruction and Explanation. https://arxiv.org/abs/1710.02714

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