  The ability to handle objects in cluttered environment has been long
anticipated by robotic community. However, most of works merely focus on
manipulation instead of rendering hidden semantic information in cluttered
objects. In this work, we introduce the scene graph for embodied exploration in
cluttered scenarios to solve this problem. To validate our method in cluttered
scenario, we adopt the Manipulation Question Answering (MQA) tasks as our test
benchmark, which requires an embodied robot to have the active exploration
ability and semantic understanding ability of vision and language.As a general
solution framework to the task, we propose an imitation learning method to
generate manipulations for exploration. Meanwhile, a VQA model based on dynamic
scene graph is adopted to comprehend a series of RGB frames from wrist camera
of manipulator along with every step of manipulation is conducted to answer
questions in our framework.The experiments on of MQA dataset with different
interaction requirements demonstrate that our proposed framework is effective
for MQA task a representative of tasks in cluttered scenario.
