Human-in-the-loop Robotic Grasping Using BERT Scene Representation
Yaoxian Song, Penglei Sun, Pengfei Fang, Linyi Yang, Yanghua Xiao, Yue Zhang
Abstract
Current NLP techniques have been greatly applied in different domains. In this paper, we propose a human-in-the-loop framework for robotic grasping in cluttered scenes, investigating a language interface to the grasping process, which allows the user to intervene by natural language commands. This framework is constructed on a state-of-the-art grasping baseline, where we substitute a scene-graph representation with a text representation of the scene using BERT. Experiments on both simulation and physical robot show that the proposed method outperforms conventional object-agnostic and scene-graph based methods in the literature. In addition, we find that with human intervention, performance can be significantly improved. Our dataset and code are available on our project website https://sites.google.com/view/hitl-grasping-bert.- Anthology ID:
- 2022.coling-1.265
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2992–3006
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.265
- DOI:
- Bibkey:
- Cite (ACL):
- Yaoxian Song, Penglei Sun, Pengfei Fang, Linyi Yang, Yanghua Xiao, and Yue Zhang. 2022. Human-in-the-loop Robotic Grasping Using BERT Scene Representation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2992–3006, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Human-in-the-loop Robotic Grasping Using BERT Scene Representation (Song et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.265.pdf
Export citation
@inproceedings{song-etal-2022-human,
title = "Human-in-the-loop Robotic Grasping Using {BERT} Scene Representation",
author = "Song, Yaoxian and
Sun, Penglei and
Fang, Pengfei and
Yang, Linyi and
Xiao, Yanghua and
Zhang, Yue",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.265",
pages = "2992--3006",
abstract = "Current NLP techniques have been greatly applied in different domains. In this paper, we propose a human-in-the-loop framework for robotic grasping in cluttered scenes, investigating a language interface to the grasping process, which allows the user to intervene by natural language commands. This framework is constructed on a state-of-the-art grasping baseline, where we substitute a scene-graph representation with a text representation of the scene using BERT. Experiments on both simulation and physical robot show that the proposed method outperforms conventional object-agnostic and scene-graph based methods in the literature. In addition, we find that with human intervention, performance can be significantly improved. Our dataset and code are available on our project website \url{https://sites.google.com/view/hitl-grasping-bert}.",
}
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%0 Conference Proceedings %T Human-in-the-loop Robotic Grasping Using BERT Scene Representation %A Song, Yaoxian %A Sun, Penglei %A Fang, Pengfei %A Yang, Linyi %A Xiao, Yanghua %A Zhang, Yue %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F song-etal-2022-human %X Current NLP techniques have been greatly applied in different domains. In this paper, we propose a human-in-the-loop framework for robotic grasping in cluttered scenes, investigating a language interface to the grasping process, which allows the user to intervene by natural language commands. This framework is constructed on a state-of-the-art grasping baseline, where we substitute a scene-graph representation with a text representation of the scene using BERT. Experiments on both simulation and physical robot show that the proposed method outperforms conventional object-agnostic and scene-graph based methods in the literature. In addition, we find that with human intervention, performance can be significantly improved. Our dataset and code are available on our project website https://sites.google.com/view/hitl-grasping-bert. %U https://aclanthology.org/2022.coling-1.265 %P 2992-3006
Markdown (Informal)
[Human-in-the-loop Robotic Grasping Using BERT Scene Representation](https://aclanthology.org/2022.coling-1.265) (Song et al., COLING 2022)
- Human-in-the-loop Robotic Grasping Using BERT Scene Representation (Song et al., COLING 2022)
ACL
- Yaoxian Song, Penglei Sun, Pengfei Fang, Linyi Yang, Yanghua Xiao, and Yue Zhang. 2022. Human-in-the-loop Robotic Grasping Using BERT Scene Representation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2992–3006, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.