MultiON: Benchmarking Semantic mAP Memory Using Multi-Object Navigation
Abstract
Navigation tasks in photorealistic 3D environments are challenging because they require perception and effective planning under partial observability. Recent work shows that map-like memory is useful for long-horizon navigation tasks. However, a focused investigation of the impact of maps on navigation tasks of varying complexity has not yet been performed.
Cite
Text
Wani et al. "MultiON: Benchmarking Semantic mAP Memory Using Multi-Object Navigation." Neural Information Processing Systems, 2020.Markdown
[Wani et al. "MultiON: Benchmarking Semantic mAP Memory Using Multi-Object Navigation." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/wani2020neurips-multion/)BibTeX
@inproceedings{wani2020neurips-multion,
title = {{MultiON: Benchmarking Semantic mAP Memory Using Multi-Object Navigation}},
author = {Wani, Saim and Patel, Shivansh and Jain, Unnat and Chang, Angel and Savva, Manolis},
booktitle = {Neural Information Processing Systems},
year = {2020},
url = {https://mlanthology.org/neurips/2020/wani2020neurips-multion/}
}