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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.

Embodied agents are expected to perform more complicated tasks in an interactive environment, with the progress of Embodied AI in recent years. Existing embodied tasks including Embodied Referring Expression (ERE) and other QA-form tasks mainly focuses on interaction in term of linguistic instruction. Therefore, enabling the agent to manipulate objects in the environment for exploration actively has become a challenging problem for the community. To solve this problem, We introduce a new embodied task: Remote Embodied Manipulation Question Answering (REMQA) to combine ERE with manipulation tasks. In the REMQA task, the agent needs to navigate to a remote position and perform manipulation with the target object to answer the question. We build a benchmark dataset for the REMQA task in the AI2-THOR simulator. To this end, a framework with 3D semantic reconstruction and modular network paradigms is proposed. The evaluation of the proposed framework on the REMQA dataset is presented to validate its effectiveness.