  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 rasping 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.
