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.
@article{arxiv.1710.02714,
title = {Interactive Learning of State Representation through Natural Language Instruction and Explanation},
author = {Qiaozi Gao and Lanbo She and Joyce Y. Chai},
journal= {arXiv preprint arXiv:1710.02714},
year = {2017}
}