Name
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. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Volume | Citations | PageRank |
Proceedings of the 29th International Conference on Computational Linguistics | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaoxian Song | 1 | 0 | 0.34 |
| Penglei Sun | 2 | 0 | 0.34 |
| Pengfei Fang | 3 | 0 | 0.34 |
| Linyi Yang | 4 | 1 | 2.38 |
| Yanghua Xiao | 5 | 0 | 1.01 |
| Yue Zhang | 6 | 1364 | 114.17 |