LeRet: Language-Empowered Retentive Network for Time Series Forecasting
Abstract
As robots' manipulation capabilities improve for pick-and-place tasks (e.g., object packing, sorting, and kitting), methods focused on understanding human-acceptable object configurations remain limited expressively with regard to capturing spatial relationships important to humans. To advance robotic understanding of human rules for object arrangement, we introduce positionally-augmented RCC (PARCC), a formal logic framework based on region connection calculus (RCC) for describing the relative position of objects in space. Additionally, we introduce an inference algorithm for learning PARCC specifications via demonstrations. Finally, we present the results from a human study, which demonstrate our framework's ability to capture a human's intended specification and the benefits of learning from demonstration approaches over human-provided specifications.
Cite
Text
Huang et al. "LeRet: Language-Empowered Retentive Network for Time Series Forecasting." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/460Markdown
[Huang et al. "LeRet: Language-Empowered Retentive Network for Time Series Forecasting." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/huang2024ijcai-leret/) doi:10.24963/ijcai.2024/460BibTeX
@inproceedings{huang2024ijcai-leret,
title = {{LeRet: Language-Empowered Retentive Network for Time Series Forecasting}},
author = {Huang, Qihe and Zhou, Zhengyang and Yang, Kuo and Lin, Gengyu and Yi, Zhongchao and Wang, Yang},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {4165-4173},
doi = {10.24963/ijcai.2024/460},
url = {https://mlanthology.org/ijcai/2024/huang2024ijcai-leret/}
}