Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction
Abstract
We propose an approach for mapping natural language instructions and raw observations to continuous control of a quadcopter drone. Our model predicts interpretable position-visitation distributions indicating where the agent should go during execution and where it should stop, and uses the predicted distributions to select the actions to execute. This two-step model decomposition allows for simple and efficient training using a combination of supervised learning and imitation learning. We evaluate our approach with a realistic drone simulator, and demonstrate absolute task-completion accuracy improvements of 16.85% over two state-of-the-art instruction-following methods.
Cite
Text
Blukis et al. "Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction." Conference on Robot Learning, 2018.Markdown
[Blukis et al. "Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction." Conference on Robot Learning, 2018.](https://mlanthology.org/corl/2018/blukis2018corl-mapping/)BibTeX
@inproceedings{blukis2018corl-mapping,
title = {{Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction}},
author = {Blukis, Valts and Misra, Dipendra Kumar and Knepper, Ross A. and Artzi, Yoav},
booktitle = {Conference on Robot Learning},
year = {2018},
pages = {505-518},
url = {https://mlanthology.org/corl/2018/blukis2018corl-mapping/}
}